317 results about "Radial basis function neural" patented technology

The invention provides an earth and rockfill dam seepage pressure prediction method based on mechanism and data dual drive, and the method comprises the following steps: generating multi-working-condition seepage pressure data, optimizing a radial basis function neural network through an aurora optimization algorithm, and constructing an efficient proxy model; on the basis of a proxy model prediction result, fitting is carried out by combining measured data, and a seepage pressure prediction mechanism model based on a hysteresis effect function is established; with the predicted value of the mechanism model as a label, supervising the training model to learn the physical law of the seepage field; and freezing the physical feature extraction layer in the trained model, and finely adjusting the time sequence modeling layer by using actually measured data to realize approximation of an actually measured value. According to the method, mechanism driving and data driving are organically combined, the interpretability of a physical rule is reserved, complex factors which are not considered by a mechanism model are complemented by utilizing actually measured data, and the generalization ability of a deep learning model under extreme working conditions is remarkably improved while the prediction precision of the model is ensured.

The invention provides an intelligent fault-tolerant formation method for triggering a sea-air heterogeneous unmanned system by a preset time event. According to the technical scheme, the method comprises the following steps: step 1, converting an under-actuated heterogeneous USV-UAV system into a full-driven second-order dynamic system by using coordinate transformation; 2, designing a self-adaptive preset time dynamic event triggering mechanism; 3, designing a preset time sliding mode intelligent fault-tolerant formation control algorithm based on distributed errors; 4, designing an event triggering preset time intelligent fault-tolerant formation control algorithm based on a radial basis function neural network minimum learning parameter method; and step 5, carrying out stability proving on the designed intelligent fault-tolerant formation control algorithm, and eliminating a sesame phenomenon. The method has the beneficial effect that the communication efficiency is greatly improved through a self-adaptive dynamic event triggering mechanism.

A robot kinetic parameter identification method based on double-layer iteration and friction compensation comprises the following steps: S1, establishing a kinetic model of a robot, and performing linearization processing on the kinetic model to obtain a linearization model represented by an observation matrix and an inertial parameter vector; s2, designing an excitation trajectory for the linearized model by adopting improved Fourier series of a quintic polynomial, and setting constraint conditions of joint positions, speeds and accelerated speeds; s3, the robot is controlled to move according to the excitation track, and joint state data of the robot are collected and subjected to noise reduction processing; and defining the noise-reduced driving torque as a measurement torque. According to the robot kinetic parameter identification method based on double-layer iteration and friction compensation, the physical feasibility of the robot kinetic parameters can be ensured, the friction model is improved to identify the friction parameters, the friction model is fitted by adopting the radial basis function neural network, and the precision of subsequent robot control is ensured.

The invention discloses a ground surface settlement measurement data real-time fitting method based on an evolutionary algorithm. The method comprises the following steps: S1, acquiring a ground surface settlement measurement data set; s2, preprocessing the ground surface settlement measurement data set; s3, generating a ground surface settlement measurement data feature matrix; s4, constructing a radial basis function neural network model for ground surface settlement measurement data fitting; s5, optimizing parameters of the initial ground surface settlement measurement data fitting function by adopting an alpha evolutionary algorithm; and S6, adopting the optimized ground surface settlement measurement data fitting function to predict the input ground surface settlement measurement data. According to the method, the calculation efficiency, the nonlinear expression capability and the global optimization capability of ground surface settlement measurement data fitting are effectively improved, and the method can be widely applied to multiple scenes of ground surface settlement monitoring, infrastructure safety evaluation and mining area collapse prediction.

A system, computer readable storage medium and method for controlling a trajectory of coordinated time-varying maneuvers of a geometric formation of unmanned vehicles is disclosed. The system includes unmanned vehicles, each configured with communication circuitry to communicate between the vehicles. A subset of the unmanned vehicles function as leader vehicles, with the remaining vehicles functioning as follower vehicles for leader-follower maneuvering. The system further includes an actuator suite configured to adjust the direction and orientation of each vehicle, a sensor suite for stabilization and navigation, and a flight controller for maintaining stable maneuvering, even in the presence of actuator faults and sensor deception attacks. Processing circuitry is configured with a reinforcement learning neural network that includes identifier, actor, and critic radial basis function neural networks to estimate movement, adjust control actions, and assess vehicle performance based on feedback signals, including corrupted signals from the sensor suite due to deception attacks.

The invention provides an unmanned aerial vehicle-unmanned vehicle combined formation cooperative control method and system, and relates to the technical field of vehicle-vehicle cooperation, and the method comprises the steps: taking a virtual unmanned aerial vehicle as a leader, taking the unmanned aerial vehicle and the unmanned vehicle as followers in a unified manner, setting a fixed formation offset for each follower, and defining a communication topological relation based on a graph theory; the method comprises the following steps: acquiring an actual measurement state vector of a sensor in real time, processing a current estimation state through a constructed radial basis function neural network observer, outputting a disturbance estimation value in combination with a weight matrix, updating the estimation state according to a core equation, and judging whether to update the weight matrix or not according to an observation error; calculating the expected state of the follower according to the reference trajectory of the leader and the fixed formation offset, calculating the formation error, constructing an event trigger communication condition, judging whether the condition is met or not, enabling the follower to interact the state and the error according to the communication topology only when the condition is met, or else, continuing to use the previous trigger data; and finally, current control input of the follower is calculated through a distributed control law.

The invention belongs to the technical field of photoelectric stabilized platform intelligent control, and particularly relates to a photoelectric stabilized platform composite anti-interference method based on improved active-disturbance-rejection control, which realizes multi-band disturbance cooperative suppression through an improved extended state observer (ESO) and non-linear state error feedback (NLSEF) core architecture. For the problems of phase distortion and parameter sensitivity under traditional ADRC high-frequency disturbance, an ALMRBF-ESO observer is constructed by adopting a dynamic regularization constrained radial basis function neural network and an adaptive damping adjustment mechanism, gradient dispersion limitation of a traditional RBF in a high-frequency domain is broken through, and disturbance root-mean-square errors are reduced; by reconstructing a sliding mode gain equation of nonlinear tracking error feedback and implanting a parameter self-correction mechanism, dynamic response of a system is accelerated, overshoot is reduced at the same time, and the parameter drift rate of a controller is stabilized at 0.12. According to the method, on the basis of completely reserving ADRC robustness, the problem of collaborative optimization of ESO broadband disturbance observation precision and NLSEF dynamic tracking performance is solved.

The invention relates to the technical field of inverter control, and discloses a micro inverter control method and system in a photovoltaic system and a storage medium. The method comprises the following steps: carrying out parameter acquisition and processing on the photovoltaic micro inverter to obtain an initial parameter set; designing a radial basis function neural network compensation uncertainty parameter based on the initial parameter set; inputting the compensation parameter set into a particle swarm optimization algorithm to optimize control parameters; and training a back propagation neural network based on the optimized parameter set to realize maximum power point tracking control. According to the method, the uncertainty of the system is compensated by introducing the radial basis function neural network, the control parameters are optimized in combination with the particle swarm optimization algorithm, and the real-time prediction and adjustment of the parameters are realized by using the back propagation neural network; the technical problems that a traditional control method is low in control precision and poor in adaptability when facing nonlinear system characteristics, environmental condition changes and parameter uncertainty are effectively solved.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention relates to the technical field of robot control, in particular to a cross-modal perceptual driving body robot compliance control system which comprises the steps that a sensor is adopted to synchronously collect environment information, multi-source data bias is eliminated through a space-time alignment algorithm, a radial basis function neural network is adopted to analyze multi-modal fusion features, and a multi-modal model is obtained; human operation intention probability distribution is extracted to decompose a task into a path planning layer and a motion control layer, a collision-free trajectory is generated through an RRT algorithm, a high-fidelity physical engine is adopted to construct a virtual interaction scene, and robot learning results are shared through federal learning. According to the method, the problems of inaccurate perception, incoordination between intention recognition and interaction control, difficulty in control strategy verification, slow new task adaptation and difficulty in multi-robot learning result sharing caused by multi-source data deviation and large multi-modal semantic difference of the body robot in a complex environment are solved.

The invention discloses a self-adaptive brushless motor control method and system, and relates to the field of intelligent control, and the method comprises the steps: collecting the original data of the operation state of a motor through a sensor group, and carrying out the preprocessing; time-varying parameter identification is completed through combination of an extended Kalman filtering algorithm and a radial basis function neural network, an evaluation index system is constructed based on an analytic hierarchy process to obtain a comprehensive evaluation value, and a related trend is predicted through a long and short-term memory neural network; constructing a multi-modal control strategy library, determining an adaptive strategy, optimizing core parameters by using an improved particle swarm optimization algorithm, generating a control instruction, and outputting a corresponding current through a power driving module; and monitoring motor parameters in real time, comparing with a control target value, calculating deviation, correcting an identification result, adjusting a strategy threshold value, and updating and optimizing an objective function. The method has the advantages that by accurately sensing the state of the motor, dynamically adapting the control strategy and optimizing parameters in real time, it is ensured that the motor stably and efficiently operates under the complex working condition, and the characteristics of energy conservation and long service life are achieved.

The invention relates to the technical field of thermal protection of hypersonic aircrafts, in particular to a multi-scale prediction method for an ablation behavior of a thermal protection structure based on data driving, which comprises the following steps: performing molecular dynamics simulation on a gas-solid interface of an ablation type thermal protection material to obtain simulation calculation activation energy; obtaining the experimental activation energy of the material; performing Bayesian optimization on the simulation calculation activation energy and the experiment measured activation energy to obtain optimized activation energy; performing macroscopic wall surface ablation simulation by taking the optimized activation energy as a boundary condition to obtain an ablation parameter database recording ablation wall surface temperatures, ablation rates and ablation depths corresponding to different flow rates, altitudes and time; training a radial basis function neural network based on the ablation parameter database, and establishing an ablation prediction model; obtaining a prediction result; the calculation efficiency and accuracy of the thermal protection structure ablation behavior multi-scale prediction method can be improved.

The invention discloses an exoskeleton robot fixed time control method based on output constraint and disturbance observation, relates to the field of robot control, constructs a fixed time neural controller (FTNC), and ensures that a system is stable in fixed time by combining a universal obstacle Lyapunov function (UBLF) and a preset performance function (PPF), so as to improve the system stability. The convergence time is irrelevant to the initial state; meanwhile, a radial basis function neural network (RBFNN) and a nonlinear disturbance observer (NDO) are adopted to jointly compensate the uncertainty of the model and the man-machine interaction disturbance; by dynamically adjusting UBLF boundary conditions, joint angle errors are limited, and training safety is guaranteed. According to the method, a fixed time control theory is adopted, fixed time is combined with a preset performance function (PPF) and a universal barrier Lyapunov function (UBLF), and the output of the exoskeleton robot is strictly limited, so that the rapid convergence and transient performance requirements of an exoskeleton system are met at the same time.

The invention discloses a low-coherence interference demodulation method based on modal decomposition and a radial basis function neural network, and the method comprises the steps: carrying out the empirical mode decomposition of a filtered low-coherence interference signal, extracting the effective time-frequency domain features of each IMF and the mathematical statistics time domain features of the low-coherence interference signal, and forming a low-coherence interference signal feature data set; constructing and training a radial basis neural network, establishing a nonlinear model between low-coherence interference signal features and pressure, setting an input layer to correspond to a fusion feature vector, setting a hidden layer to adopt a radial basis function as an activation function, realizing nonlinear mapping by using a Gaussian kernel function, and outputting a layer to correspond to a pressure value; the network is trained through the feature data set, a mean square error is used as a loss function, a gradient descent method is adopted to optimize the weight and threshold of the network, and iterative training is carried out until the error converges; and inputting a low-coherence interference signal acquired by an optical fiber Fabry-Perot pressure demodulation device, and demodulating the signal through the trained radial basis function neural network model to obtain a corresponding pressure value.

The invention discloses a J-A model parameter identification method, system, equipment and medium based on RBF and an improved brownish bear algorithm, and belongs to the technical field of power system optimization, and the method comprises the steps: building a Jiles-Atherton hysteresis reverse model of a current transformer, determining a to-be-identified parameter vector, and building a model with a root-mean-square error between actually measured magnetic field intensity and simulated magnetic field intensity as a target function, training a radial basis function neural network model, expanding data through linear interpolation processing, obtaining a predicted magnetic induction intensity value, inputting an objective function and radial basis function prediction data into an improved brownish bear optimization algorithm, and iteratively optimizing model parameters through hierarchical population position updating and fitness evaluation until convergence conditions are met. And outputting an optimal parameter identification result. According to the method, high-precision and high-efficiency identification of hysteresis model parameters is realized, the generalization capability and robustness of the system are improved, and reliable technical support is provided for hysteresis characteristic analysis of a complex physical system.

The invention discloses a modular mechanical arm actuator additive fault optimal fault-tolerant control method and device of a zero sum differential game, and relates to the field of robot control algorithms, and the method comprises the steps: representing a nonlinear damping characteristic through a joint friction torque, describing the dynamic interaction of multiple joints through a cross-linking coupling item, and determining the optimal fault-tolerant control of the additive fault of the modular mechanical arm actuator; a dynamical model containing faults is constructed. And uncertain items in the model are updated online by adopting a radial basis function neural network identifier, so that the model precision is improved. A performance index function is constructed based on position errors, actuator faults and controller input are regarded as two opposite parties of a zero and differential game, the performance index function is approximated through a single evaluation neural network, a Hamiltonian-Jacobi-Axaxi equation is approximately solved, and an optimal fault-tolerant control strategy is obtained. According to the method, the game theory is combined with the neural network, the dynamic unknown fault problem of the modular mechanical arm is effectively solved while the system energy consumption integration is reduced, and real-time optimal control over the modular mechanical arm is achieved.

The invention relates to a method and device for predicting online open course learner satisfaction and electronic equipment, and the method comprises the steps: predicting the online open course satisfaction of students through an MLP and RBF neural network model by using a virtual learning environment of a large-scale online teaching and learning platform and combining learning behavior data in a learning management system (LMS); the model comprises a data acquisition and processing module, a multilayer perceptron (MLP) module, a radial basis function (RBF) neural network module, a classification tree module and a control block, the data acquisition and processing module is used for generating a training and testing data set, and the MLP and RBF neural network model predicts the satisfaction degree of a learner. The MLP model carries out feature extraction through a multi-layer perceptron structure and different activation functions, the RBF model measures the distance between input data and a center by using a radial basis function to realize feature extraction, the classification tree is used for judging a prediction model to which a data point belongs, the control block integrates features from the MLP and RBF neural network models, and the RBF model is used for determining a prediction model to which the data point belongs. Experimental results show that the prediction accuracy of low-satisfaction-degree learners and high-satisfaction-degree learners can be improved at the same time through the combination scheme of the MLP and the RBF, the method can be applied to learner satisfaction degree prediction of various online open courses, an educational institution is helped to know the satisfaction degree condition of students in time, course design and teaching strategies are optimized, and the teaching efficiency is improved. And important support is provided for teaching reform and optimization in the field of online education.

The invention discloses a mechanical arm trajectory tracking algorithm based on an improved sliding mode adaptive neural network, and belongs to the technical field of mechanical arm control. The method comprises the steps that recorded data of an improved sliding mode and a neural network about the mechanical arm and estimated values under the influence of actual modeling errors and external disturbance are obtained, and a mechanical arm dynamic model is jointly constructed; through the collaborative design of dynamic modeling optimization, sliding mode control improvement and adaptive RBF neural network compensation, the three core problems of low precision, large buffeting and poor reliability in mechanical arm trajectory tracking are synchronously solved; the radial basis function neural network is adopted for online estimation, the influence of modeling errors and external unknown disturbance on a mechanical arm system is avoided, the trajectory tracking precision is improved, and meanwhile it is guaranteed that all joint angles operate stably, and sudden change of torque does not exist.

The invention discloses a fault detection method and system for an EVTOL aircraft, and relates to the technical field of fault detection, and the method comprises the steps: extracting fault data from operation data through employing a radial basis function neural network, and building a fault data set; constructing a generative adversarial network, generating virtual fault data, and aligning the virtual fault data with actual fault data by using a domain adaptation technology to expand a fault data set; constructing a fault propagation model, simulating a dynamic propagation path of a fault between components in the electric vertical take-off and landing aircraft, and optimizing a fault recognition threshold by combining a federated learning framework; and constructing a hypergraph structure, processing a fault propagation path in the hypergraph structure by using a gated attention propagation network, and outputting a fault diagnosis result in combination with the optimized fault recognition threshold. According to the method, feature extraction and anomaly recognition are performed on the collected data through the radial basis function neural network, and the accuracy of fault detection and the adaptability to different working conditions are effectively improved.

The invention discloses a multi-stage control method for a lower limb exoskeleton, is applied to the field of exoskeleton robots, and aims to solve the problem that the design control precision is reduced due to the uncertainty of a model in the prior art. According to the controller designed by the invention, in a high-level control layer, a human body training mode is determined by a motion intention of an operator so as to generate a reference gait track; in the middle control layer, a variable admittance controller is designed, and three training modes of human exoskeleton cooperative movement, namely a passive mode, an active mode and a passive-to-active mode, are planned. In a low-level control loop, a method with a radial basis function neural network estimation function and a fixed-time convergence controller with input dead zone compensation are provided so as to ensure that an exoskeleton joint position tracks an expected trajectory output by an admittance loop. In order to avoid the Zeno phenomenon of the designed controller, an event trigger mechanism is used to determine the execution time of sampling and transmitting signals. Finally, the effectiveness of the proposed control strategy is verified through simulation and experimental results.

The invention belongs to the technical field of charging station planning, and discloses a charging station optimal configuration method and equipment, and the method comprises the following steps: constructing a target function with the minimization of total power loss and the minimization of total voltage deviation as targets; constructing a charging station locating and sizing model by taking a system power flow equality constraint and a node voltage inequality constraint as constraint conditions; identifying a nearest minimum power loss charging point through an arithmetic optimization algorithm; and predicting the electric vehicle charging demand of the charging station through a radial basis function neural network algorithm to obtain preliminary prediction of charging station site selection, and solving the charging station constant volume site selection model to obtain the optimal capacity and position of the charging station. According to the method, the arithmetic optimization algorithm and the radial basis function neural network are combined to optimize the layout configuration of the electric vehicle charging station, the power loss of a power distribution network system can be reduced to the maximum extent, the voltage stability is improved, the charging demand change can be dynamically reflected, and the calculation efficiency is improved.

The invention discloses an unmanned ship adaptive optimal interference control method based on reinforcement learning, and particularly relates to the technical field of unmanned ship automatic control, and the method comprises the steps: building an unmanned ship trajectory kinematics model based on the position information and heading angle information of an unmanned ship under a geodetic coordinate system, and the corresponding speed information under an unmanned ship appendage coordinate system; the method comprises the following steps: establishing an unmanned ship trajectory dynamics model by considering the control operation of the unmanned ship and the time-varying environment interference problems of wind, waves, flow, unmodeled dynamics and the like in a marine environment in which the unmanned ship is located; introducing a radial basis function neural network based on a set unmanned ship trajectory mathematical model; designing a self-adaptive interference observer to estimate and offset time-varying environment interference in unmanned ship trajectory tracking; based on a radial basis function neural network and an interference observer, an unmanned ship adaptive interference suppression controller is designed by using an adaptive vector backstepping method.

The invention belongs to the technical field of power distribution automation of a power system, and particularly relates to a self-adaptive load control method and system for an intelligent cable branch box, and the method comprises the steps: collecting and preprocessing the current data of a branch loop and the temperature data of a cable joint in real time, obtaining a data confidence factor, and extracting a current change rate and a temperature rise rate; obtaining a load prediction value in a future set time period through a load trend prediction model; calculating the residual heat tolerance time for reaching the limit tolerance temperature; calculating an output load control adjustment coefficient based on the adaptive load control decision model and a particle swarm optimization algorithm; and judging a current state interval according to the load control adjustment coefficient, and controlling an execution end to execute a grading response strategy of through-flow maintenance, short-time overload monitoring and early warning and breaking current limiting in combination with the residual heat tolerance time. According to the method, the residual heat tolerance time is calculated in combination with load prediction and a transient thermal circuit model, and hierarchical response of load control is realized through a particle swarm optimized radial basis function neural network decision.

The invention provides a flying vehicle path planning method based on dynamic weight adjustment, and belongs to the technical field of aircraft planning. Comprising the following steps: acquiring planning data under different fixed weight values; processing planning data according to actual vehicle energy storage and task limiting time, and fitting weight mathematical representations under different task requirements by using a radial basis function neural network RBFNN (Radial Basis Function Neural Network); the method comprises the following steps: designing a weight A * algorithm of dynamic weight adjustment, dynamically adjusting weight values of different costs, searching a path node with the minimum comprehensive cost, reasonably switching different motion modes and planning a short-time energy-saving task path on the basis of weight mathematical representation and aiming at task requirements changing in real time. According to the method, reasonable switching of different motion modes is realized by searching the path node with the minimum comprehensive cost, and a short-time energy-saving task path is planned. Weight mathematical representations under different task requirements are constructed through the RBFNN, and weight values of different costs are dynamically adjusted to meet the task requirements changing in real time.

The invention provides a man-machine cooperation compliance control method based on improved deep reinforcement learning in combination with intentions of collaborators. Estimating the motion intention of the human in real time based on a radial basis function neural network; a strategy network in a traditional DDPG is replaced with a DDPG algorithm combined with GP, and optimization of impedance parameters in self-adaptive impedance control is achieved. Aiming at hyper-parameter optimization in the GP model, a k-fold cross validation method is adopted; a smooth switching strategy is adopted, and a proper strategy is selected. Finally, the flexibility of the mechanical arm is guaranteed, and meanwhile the man-machine cooperation process is safely and efficiently achieved.

The invention provides a steering-by-wire control method and system based on particle swarm-sliding mode control and a fuzzy radial basis function, and the method comprises the steps: carrying out the adaptive approximation of an uncertain item and unknown disturbance of a state-space equation based on a radial basis function neural network in combination with an adaptive law, and obtaining the real-time estimation values of the uncertain item and the unknown disturbance; a sliding mode surface is obtained based on the wheel rotation angle error, the sliding mode surface is corrected and compensated based on the real-time estimation value, parameters of the sliding mode surface are optimized in combination with a particle swarm algorithm, and an optimized sliding mode surface is obtained; on the basis of a fuzzy logic controller, the optimized sliding mode surface and the change rate of the optimized sliding mode surface are converted into membership degrees of a fuzzy set for fuzzy logic reasoning, and the fuzzy set of control behaviors is obtained; obtaining a control signal based on the fuzzy set of control behaviors; and completing steering-by-wire control of the vehicle based on the control signal. According to the technical scheme, the response speed and the anti-interference capability of the steer-by-wire system can be improved.

The invention discloses an advanced dynamic evaluation method for dolomite sanding characteristics. The advanced dynamic evaluation method comprises the following steps: determining mesoscopic and macroscopic thresholds of dolomite sanding degree grading as rock test static parameters based on an indoor test; extracting kinetic parameters and electrical parameters of the dolomite rock mass as advanced detection dynamic parameters based on geophysical advanced detection of missile-electricity combination; performing principal component analysis and correlation analysis on the rock test static parameters and advanced detection dynamic parameters, and constructing a dolomite sanding dynamic-static parameter quantitative conversion model by using RBFNN (Radial Basis Function Neural Network); training an intelligent grading evaluation model by taking the rock test static parameters as input and the sanding degree grade as output; and mapping the advanced detection dynamic parameters into rock test static parameters through the quantitative conversion model, inputting the mapped rock test static parameters into the intelligent grading evaluation model for verification and iterative optimization, and outputting a sanding degree grading result. Advanced prevention and control of sanding of the dolomite in front of the tunnel construction face can be achieved, and the ground disaster of sand collapse caused by sanding and sand gushing is avoided.

The invention discloses a time sequence reconstruction and machine learning coupled chlorophyll monitoring data breakpoint restoration method, and belongs to the technical field of water quality monitoring. The invention discloses a chlorophyll monitoring data breakpoint restoration method based on coupling of time sequence reconstruction and machine learning, and the method comprises the following steps: S1, collecting water quality monitoring data, and cleaning the monitoring data to obtain preprocessed data; s2, performing time sequence reconstruction on the preprocessed data to obtain a weekly average 1 data set; s3, respectively constructing a radial basis function neural network model and a back propagation neural network model by taking the chlorophyll concentration as a response variable and the conventional water quality parameter as a predictive variable; s4, performing performance evaluation on each model by taking a root mean square error, an average absolute percentage error, goodness of fit and relative error distribution statistics as evaluation indexes, and screening out an optimal model; and S5, applying the conventional water quality parameters in the breakpoint interval of the chlorophyll monitoring data in the water body to the optimal model, and outputting the restored chlorophyll concentration value to complete the dynamic restoration of the breakpoint.

The invention provides a flexible intelligent material driver output constraint control method considering butterfly-shaped hysteresis input for an output constraint flexible intelligent material driving system with butterfly-shaped hysteresis input, aims to realize high-precision control in different application scenes, and comprises the following steps of: on the basis of a traditional KP model, selecting a KP model; according to the invention, a new butterfly-shaped KP (Butterfly Kranoselskii-Pokrovskii, BKP) kernel is obtained through derivation, and the KKP (Butterfly Kranoselskii-Pokrovskii, BKP) kernel is used as a kernel of the KKP. A new BKP model is established to describe butterfly hysteresis in the flexible intelligent material driver by performing weighted stacking on the BKP core; an unknown time delay function in the system is approached by using a radial basis function neural network and a finite coverage lemma, and the time delay problem of the control system is solved; a high-gain K-filter is designed to overcome the problem that the state in the system cannot be measured, and the problem of output constraint control of the butterfly hysteresis system is solved by combining a barrier Lyapunov function and an adaptive dynamic surface output feedback control algorithm; a butterfly pseudo-inverse algorithm is designed, the requirement for solving a hysteresis inverse model is avoided, and butterfly hysteresis nonlinearity is weakened to a great extent. Compared with a traditional control scheme, the method has a better effect on tracking performance and tracking errors, the tracking errors are effectively reduced, the control precision is remarkably improved, and precise control over the intelligent material driving system is achieved.

The invention belongs to the technical field of CFD (computational fluid dynamics) parameter calibration, and discloses a CFD parameter adaptive calibration method and system based on measured data and a double-agent model, and the method comprises the steps: obtaining a CFD input parameter sample, inputting the CFD input parameter sample into a CFD solver, and obtaining an initial simulation result; determining an error evaluation index according to the initial simulation result based on a target actual measurement data result; constructing a double-agent model based on a Kriging model and a radial basis function neural network by taking a CFD input parameter sample as an independent variable and an error evaluation index as a dependent variable; the double-agent model is trained, the trained double-agent model takes the error evaluation index as fitness, and CFD input parameter values are obtained based on a genetic algorithm; the CFD input parameter values are input into the CFD solver for a simulation experiment, a calibrated simulation result is output, the reliability and generalization ability of prediction are improved through a double-agent model, a high-fidelity simulation result is output through the CFD solver, and the number of times of calling the CFD solver is reduced while the calibration precision is guaranteed.