381 results about "Radial basis function neural" patented technology

The invention provides a lithium battery health state prediction method and system based on fusion health indicators, and the method comprises the steps: obtaining lithium battery charging and discharging parameters and a data set, extracting indirect health indicators for reflecting the degradation characteristics of a battery, and verifying the reasonability of the indirect health indicators through a correlation analysis method; performing dimension reduction processing on the indirect health index by using a KPCA method to obtain first principal component data, constructing a lithium battery health state comprehensive estimation model, and further obtaining a battery health state; and decomposing an original battery capacity sequence into a local fluctuation sequence and a global degradation sequence, performing prediction by using a radial basis function neural network and Gaussian process regression, and performing superposition to obtain a battery residual life prediction value. Aiming at the problems of low accuracy, unstable model and the like in the prior art, the battery health state is measured and calculated by adopting a mode based on the fusion health indexes, the method has the advantages of high estimation precision, good application prospect and the like, and the prediction accuracy is effectively improved.

The invention provides a power cable joint defect detection method and system based on a digital twin model. The method comprises the following steps: acquiring performance parameters, temperature data and ultrasonic parameters of a cable joint; constructing a cable joint multi-field coupling simulation model according to the physical size material parameters of the cable joint; constructing an objective function according to the performance parameters and the temperature data, interacting data of the multi-field coupling simulation model with data under actual operation conditions through the objective function, and updating a pre-constructed cable joint digital twinborn simulation model to obtain a real-time updated cable joint digital twinborn simulation model; and training and testing the radial basis function neural network based on the output data of the cable joint digital twin simulation model updated in real time to obtain a fault diagnosis model of cable joint temperature and ultrasonic data based on deep learning, and detecting the real-time operation state of the cable joint according to the fault diagnosis model. And predicting a potential fault of the cable joint.

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 a stabilization control method and system for a fuel filling ship at a zero navigational speed. The method provided by the invention comprises the following steps: acquiring rolling data and wind wave data of a refueling ship under a target sea condition in a fuel refueling process in real time; the rolling data and the wind wave data are processed based on a radial basis function neural network, and the precession angular velocity of the stabilization gyro device under the target sea condition is obtained through calculation; the stabilization gyro device is controlled to rotate at the precession angular speed, the stabilization gyro device generates a gyroscopic effect and generates rolling moment, the rolling moment is converted into stabilizing moment for resisting rolling through precession motion, and the rolling angle of the filling ship is reduced; and the stabilizing moment is opposite to the vibration direction of the rolling. According to the method and system, rolling control of the filling ship at the low speed or the zero navigational speed is achieved, the stabilization torque can be adjusted in real time according to sea wave changes, and the stability and safety of the filling ship in the fuel filling operation process are improved.

A method includes: calculating a first difference between a current actual attitude of a fuselage and a desired attitude and a second difference between an actual depth and a desired depth; inputting the first difference and the second difference into a set terminal sliding mode surface to obtain an output value of the terminal sliding mode surface; using the output value as an input of a preset high-order observer, a radial basis function neural network and a terminal sliding mode control law, respectively, and using an output of the high-order observer and an output of the radial basis function neural network as a compensation input of the terminal sliding mode control law; performing power distribution for each propeller of a propeller assembly on the basis of the virtual force to obtain a propelling force of each propeller; and controlling the propellers of the underwater submersible robot.

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 relates to the technical field of PCBA board anti-interference, and discloses a vehicle-mounted display PCBA board anti-interference method, device and equipment. The method comprises the following steps: carrying out electromagnetic interference data acquisition on a PCBA (Printed Circuit Board Assembly) of the vehicle-mounted display to obtain an electromagnetic interference data set, and constructing a PCBA structure matrix; performing feature extraction to obtain interference feature data; constructing a probability distribution function, generating a feature transformation matrix and performing joint operation to obtain an interference suppression matrix; constructing a multivariable constraint condition and solving an anti-interference optimization parameter; inputting the anti-interference optimization parameters and the interference signal matrix into a particle swarm-radial basis function neural network for training to obtain an anti-interference neural network model; and performing filtering processing and compensation correction on the original input signal of the PCBA board through the anti-interference neural network model to obtain a target output signal. Compensation correction can be dynamically carried out, the compensation coefficient is adjusted in real time according to the signal error, and the stability and accuracy of the output signal are 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 discloses a grain condition data prediction method and device, and relates to the technical field of grain, and the method comprises the steps: carrying out the data cleaning processing of original grain condition data, so as to generate a grain condition data optimal attribute set; training the grain condition data optimal attribute set through a radial basis function (RBF) neural network to generate a grain condition data prediction model; and outputting predicted grain condition data within a specified time step length through the grain condition prediction optimal attribute set and the grain condition data prediction model, and performing early warning according to a comparison result of the predicted grain condition data and a preset safe grain storage critical value. According to the invention, the future development or change trend of the grain condition is predicted through the multi-type grain condition data, so that early warning is carried out on the possible dangerous grain condition to ensure the grain storage safety.

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 discloses a microcomputer host aging test method and system, and relates to the technical field of computer performance tests.The microcomputer host aging test method comprises the steps that to-be-tested equipment is connected to a test system, system standby test is conducted, and initial reference data are collected; constructing an aging prediction model and optimizing a model training strategy in combination with course learning and transfer learning; and performing a host preheating test and a high-load test by using AIDA64 based on a prediction result, synchronously operating a Heven Benchmark tool, and testing the graphic processing capability of the computer. The method combines a radial basis function neural network aging prediction model, carries out model training through curriculum learning and transfer learning, intelligently analyzes the CPU / GPU degradation trend, combines a quantum random number generator to generate a random load mode, simulates load fluctuation in an actual application scene, and improves the test trueness. In addition, a BIOS optimization strategy is provided to realize intelligent aging prediction and optimization regulation and control, so that the long-term stability and the service life of the microcomputer host are improved.

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 discloses a mechanical arm iterative learning control method based on a radial basis function network. The method solves the limitation that a traditional mechanical arm control method deals with nonlinear characteristics and external disturbance problems in a complex dynamic environment. The method provided by the invention comprises the following steps: firstly, constructing a mechanical arm system dynamic model, and dynamically correcting and optimizing a reference track by adopting a dynamic correction strategy; then designing a radial basis function neural network to construct a nonlinear compensation item, and designing a dynamic weight parameter and a dynamic learning gain for optimizing the performance of the controller; and finally, designing an iterative learning controller, and verifying the stability and error convergence of a control algorithm. Through the radial basis function network, the dynamic adjustment strategy and the iterative learning strategy, the control precision, the adaptive capacity and the error convergence speed of the mechanical arm system are improved.

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 an efficient autonomous three-dimensional path planning method and system for a heavy-load low-altitude aircraft, path planning is realized through an improved A * algorithm, firstly, a planning area is rasterized, an obstacle avoidance area is defined, and node search and cost value calculation are only performed in the obstacle avoidance area, so that the calculation amount is reduced. The node cost value is jointly determined by heuristic estimation of a known operation distance and an unknown operation distance and motion energy consumption cost, and the path searching efficiency and quality of the aircraft are optimized. In the path post-processing stage, redundant steering nodes are removed through a key path node screening mechanism, and a more efficient path is obtained. In addition, an efficient learning quantification method of non-structural pruning optimization is introduced, and accurate prediction of the energy consumption of the aircraft is realized through an improved radial basis function neural network. The method has the advantages that the method is not only suitable for autonomous navigation of low-altitude aircrafts, but also can be widely applied to path planning and energy consumption optimization in various fields of unmanned aerial vehicles, automatic piloting aircrafts and the like.

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 method for controlling intelligent equipment of an electric smelting furnace for magnesia based on neural network self-adaptive PID, and relates to the field of industrial control of equipment with great energy consumption. According to the method, modeling is carried out on the electric smelting magnesium furnace, the current working condition and the production boundary condition are analyzed to obtain an ideal current set value, and a corresponding PID controller is designed with the purpose of eliminating the error between the current three-phase electrode current and the set value. And the three-phase electrode current can rapidly track the changing set current value, so that the safe and stable operation of the production process is ensured. Designing a self-adaptive control method based on a radial basis function neural network; designing a self-adaptive law aiming at meeting Lyapunov stability conditions, and approaching key variables of the self-adaptive law by the radial basis function neural network; and finally, in combination with PID parameters and an adaptive law, obtaining the dynamic PID controller of the electrical smelting furnace for magnesia, which is embedded with the neural network and has self-tuning and self-adaptive gains. Compared with the prior art, the neural network adaptive PID controller has the advantages that the control effect is obviously improved, and the transient and steady-state performance of the system can be effectively improved.

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 relates to a lower limb motion intention recognition method based on surface electromyogram (sEMG) fusion features. An sEMG feature fusion model and a genetic radial neural network model are included. The sEMG is widely applied to gait analysis and robot control, and due to the fact that the accuracy rate of intention recognition is low and development of an exoskeleton robot is limited, a genetic radial neural network algorithm based on sEMG multi-feature fusion is provided to achieve accurate recognition of eight lower limb actions. An original sEMG signal is collected through a gait experiment, and time domain, frequency domain and wavelet decomposition signal characteristic parameters of the original sEMG signal are extracted and fused. And using a principal component analysis method to carry out dimension reduction on fusion features, and inputting sEMG fusion feature vectors into a radial basis function neural network optimized by a genetic algorithm to carry out training and identification. According to the method provided by the invention, the sEMG fusion features are utilized to identify the human body lower limb actions, the accurate identification of the lower limb actions is realized in combination with a high-performance neural network algorithm, and support is provided for the application of sEMG signals to an intelligent rehabilitation robot.

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.