15 results about "Nonlinear approximation" patented technology

The invention relates to the technical field of intelligent sensing systems, in particular to an algorithm and system for avoiding water surface floating objects and improving monitoring precision based on radar water level monitoring, and the method comprises the following steps: collecting radar echoes, constructing spatial-temporal characteristics based on waveform asymmetry and coherent attenuation gradient, and calculating the spatial-temporal characteristics; according to the method, the central moments of the front edge and the rear edge of the echo are calculated, the waveform asymmetry is quantified, the dynamic stability is analyzed in combination with the attenuation gradient of the multi-time-lag coherence coefficient, the floating object interference is recognized from the double dimensions of the spatial form and the time memorability, and the actual water level is inverted. An effective scattering trailing interval is positioned, nonlinear approximation operation is performed on trailing data in combination with an index physical model, a shielded water surface energy distribution curve is reconstructed, a derivative zero point is solved, distance measurement deviation caused by floating objects is corrected, the real water level height is effectively restored, and the monitoring anti-interference capability and the data accuracy in a complex hydrological environment are improved.

The invention discloses a self-adaptive fuzzy PID flow field control method and device, and the method comprises the steps: generating an initial PID gain parameter in real time through a fuzzy reasoning module, introducing an RBF neural network module to carry out the dynamic optimization compensation of the initial parameter, and generating a correction gain. And a final PID parameter is dynamically synthesized by adopting a weighted fusion formula, and a weight coefficient is adjustable so as to balance the contribution of fuzzy rules and neural network learning. The nonlinear approximation capability of the RBF network is utilized, network parameters are updated in real time through a gradient descent method, a Jacobian matrix of a controlled system is output, and the sensitivity of the control quantity to input changes is accurately recognized. The parameter updating rate is dynamically adjusted through a momentum item and an exponential decay function in combination with environmental sensor data, flow feedback and historical deviation, and a closed-loop control loop of monitoring, fuzzy reasoning, RBF optimization, PID output and feedback is formed. The accuracy of flow field control is improved.

The application discloses a self-adaptive fuzzy PID flow field control method and device, and the method comprises the following steps: generating initial PID gain parameters in real time through a fuzzy reasoning module, introducing an RBF neural network module to dynamically optimize and compensate the initial parameters, and generating corrected gains. The final PID parameters are dynamically synthesized by using a weighted fusion formula, and the weight coefficient is adjustable to balance the contribution of fuzzy rules and neural network learning. By using the nonlinear approximation ability of the RBF network, the network parameters are updated in real time through the gradient descent method, the Jacobian matrix of the controlled system is output, and the sensitivity of the control quantity to the input change is accurately identified. In combination with the environmental sensor data, the flow feedback and the historical deviation, the parameter update rate is dynamically adjusted through the momentum term and the exponential decay function, and a closed-loop control loop of monitoring-fuzzy reasoning-RBF optimization-PID output-feedback is formed. The application improves the accuracy of flow field control.

The invention provides a drilling-expanding mixed machining material removal power prediction method based on SR-TELM, relates to the technical field of numerical control machine tool power prediction, and deeply excavates a physical mechanism between machining parameters and material removal power in drilling-expanding mixed machining material removal through a symbolic regression algorithm. By embedding a mechanism model into hidden layer neurons of the TELM, the advantage of analyzing a physical mechanism by a symbolic regression algorithm and the strong nonlinear approximation capability of the TELM are organically fused, the power prediction precision is effectively improved under a limited data condition, the physical interpretability of the model is ensured, and the power prediction efficiency is improved. And a new solution is provided for machine tool drilling-expanding mixed machining material removal power modeling.

Provided is an electronic device for controlling surface heart and a method of controlling the electronic device. The electronic device includes a speaker, a temperature sensor, a memory, and a processor electrically coupled to the speaker, the temperature sensor, and the memory. The processor obtains first temperature information based on impedance information measured in a coil included in the speaker; obtains second temperature information measured by the temperature sensor, the second temperature information based on a heat source disposed adjacent to the speaker; predicts a surface temperature of a surface area of the electronic device, opposite to an internal area in which the speaker is disposed, based on the first temperature information and the second temperature information using a nonlinear approximation function; and controls an audio signal input to the speaker based on the predicted surface temperature.

The invention discloses a neural network asymptotic tracking control method suitable for a robot system, and the method comprises the steps: introducing synaptic plasticity and internal plasticity into a conventional echo state network through simulating the adaptive characteristics of a biological nervous system, enabling the synaptic plasticity to dynamically adjust the connection weight of a neuron, enabling the internal plasticity to adaptively adjust the dynamic characteristics of the neuron, and enabling the neural network to continuously adjust the connection weight of the neuron; synchronous optimization learning of the neural network weight and the neuron state is realized, and an echo state network with a mixed learning mechanism is formed. The novel neural network is combined with a robust error symbol integral (RISE) controller, by means of the powerful nonlinear approximation capability of the neural network and the integral characteristic and the robust mechanism of the RISE controller, an unknown nonlinear part widely existing in a robot system is effectively processed, and asymptotic tracking control of the robot system on an expected trajectory is achieved. A simulation result verifies the effectiveness and stability of the method.

This invention provides a method for predicting material removal power in drilling-expansion hybrid machining based on SR-TELM, relating to the field of CNC machine tool power prediction technology. It delves into the physical mechanism between machining parameters and material removal power in drilling-expansion hybrid machining by using a symbolic regression algorithm. By embedding the mechanism model into the hidden layer neurons of TELM, it organically combines the advantages of symbolic regression in analyzing physical mechanisms with the powerful nonlinear approximation capability of TELM. This effectively improves power prediction accuracy under limited data conditions while ensuring the physical interpretability of the model, providing a new solution for modeling material removal power in drilling-expansion hybrid machining of machine tools.

The invention discloses an electric ship power adjusting device based on self-adaptive control. The electric ship power adjusting device comprises a sensor module, a data processing module, a self-adaptive control module, an actuator driving module and a power output module. The sensor module collects state parameters, operation parameters and environment disturbance information, and the state parameters, the operation parameters and the environment disturbance information are input into the self-adaptive control module after being standardized by the data processing module. The self-adaptive control module processes disturbance, non-linear terms and actuator faults through cooperative work of built-in disturbance observation, unknown non-linear approximation, fault-tolerant control and preset performance constraint units, generates self-adaptive control instructions, converts the instructions into driving signals through the actuator driving module, controls the power output module to adjust power, rotating speed and energy distribution, and controls the power output module to output power. Dynamic power adaptation of the electric ship is achieved, and navigation stability is guaranteed.

The invention discloses an unmanned aerial vehicle interference observer design method based on an RBF neural network. According to the method, the nonlinear interference observer is combined with the RBF neural network, and the real-time online learning and nonlinear approximation capabilities of the RBF neural network are utilized to perform online approximation on an estimation error generated by the nonlinear interference observer in actual operation, so that the influence of model parameter drift on the interference estimation precision is effectively weakened, and the interference estimation accuracy is improved. And the problem of noise amplification caused by high gain is avoided. According to the method, on the premise that a huge offline sample library does not need to be constructed, the improved interference observer can adaptively track various uncertain interferences in the flight process of the unmanned aerial vehicle, interference estimation errors are remarkably reduced, the real-time performance and accuracy of interference estimation are improved, a more reliable interference compensation basis is provided for an unmanned aerial vehicle control system, and the interference compensation efficiency is improved. The flight stability and control precision of the unmanned aerial vehicle in a complex dynamic environment are guaranteed, and the requirement for efficient and safe operation of the unmanned aerial vehicle is met.

The application belongs to the technical field of intelligent control, and particularly relates to a consistency learning control method based on a neural network, which comprises the following steps: S1, constructing a linear parameterization model of consistency output of multiple intelligent agents; S2, designing a loss function by outputting relevant consistency output through a neural network, and obtaining a linear parameter update algorithm in the time and iteration directions through a gradient descent method; and S3, using the output of the neural network to design a controller, and constructing a consistency learning control scheme of the multiple intelligent agent system based on neural network approximation. The scheme updates parameters based on the time axis and the iteration axis, does not require a system model and strict matrix conditions, and realizes perfect tracking in the whole time period. The application breaks through the traditional cognition of taking error as an index parameter, directly fits equivalent parameters through a neural network, solves the problems of insufficient fitting capacity of a projection algorithm and nonlinear approximation error under a complex system, and significantly improves the convergence speed of the system and the adaptability of consistency control.

This invention discloses a reinforcement learning-based attack detection method for quadcopter drones, comprising: estimating the state values ​​of the drone during flight using Kalman filtering; obtaining the corresponding residual correlation terms; dividing the entire detection space into several detection intervals; setting reward values ​​for two actions: continuing detection and detecting an attack; setting different simulated attack times; training Q-tables for different simulated attack times using the Saras algorithm; fusing the trained Q-tables for different simulated attack times using a weighted average; fitting the weighted average fused Q-table using a neural network; and detecting attacks on the drone online using the Q-table fitted by the neural network. This invention utilizes reinforcement learning technology and integrates the nonlinear approximation capability of neural networks to achieve the detection of subtle and covert attacks on quadcopter drones. It not only improves the recognition ability of subtle and covert attacks but also enables online detection of new attack patterns and reduces attack detection latency.

The application provides a neural network inversion sliding mode control method for a marine three-phase asynchronous motor, which combines the nonlinear approximation capability of an RBF neural network, good real-time performance, and the advantages of good dynamic performance and strong anti-interference of a sliding mode control, fully utilizes the back-off decoupling characteristics of a backstepping method, and designs a neural network inversion sliding mode control method for the marine three-phase asynchronous motor, so as to improve the performance of the motor control system. For the problem that the parameters cannot be determined in the modeling process of the marine three-phase asynchronous motor, a forward stability augmentation channel is designed. The backstepping method is used for decoupling control of the system, the sliding mode control solves the disturbance problem of the control system, and the RBF neural network is used for real-time estimation and compensation of the disturbance between the system coupling and the nonlinear friction force of the indirect contact surface of the control system framework.

The application discloses a kind of nonlinear system dynamic event triggering optimization control methods of reinforcement learning, it is related to triggering tracking control field, comprising the following steps: step S1, establish nonlinear multi-agent system model;Step S2, construct double-layer MLP nonlinear approximation model;Step S3, design consensus reinforcement learning control law;Step S4, design dynamic event triggering mechanism;Step S5, design weight update law and verify stability.The application adopts the nonlinear system dynamic event triggering optimization control method of reinforcement learning described above, while significantly saving communication and computing resources by using adaptive dynamic event triggering mechanism, the optimal control performance of the system is realized;Closed-loop system stability is strictly guaranteed, and Zeno behavior is effectively excluded, the effectiveness of the proposed strategy is verified by numerical testing on a multi-motor system.

The present application belongs to the technical field of hypersonic vehicle control, and relates to a reusable carrier flight-propulsion-missile coupling fault-tolerant control method. The purpose of the present application is to realize stable tracking control of the reusable carrier. The method comprises the following steps: constructing a longitudinal dynamics model of the reusable carrier; constructing a longitudinal dynamics simplified model of the reusable carrier flight-propulsion-missile; designing a sliding mode controller of the reusable carrier; and designing a RBF neural network of the reusable carrier. Through the powerful nonlinear approximation capability of the RBF neural network, the sliding mode fault-tolerant control is designed, the sensor and actuator fault-tolerant compensation is considered, and the stable tracking control of the reusable carrier is realized. The method is a reusable carrier flight-propulsion-missile coupling fault-tolerant control method, and has a wide application prospect.

The invention belongs to the technical field of hypersonic flight vehicle control, and relates to a flight-push-missile coupling fault-tolerant control method for a reusable vehicle. The invention aims to realize stable tracking control of a reusable vehicle. The method comprises the following steps: constructing a reusable vehicle longitudinal dynamic model; the invention relates to a vertical dynamics simplified model of a flying bomb of a reusable launch vehicle. Designing a reusable vehicle sliding mode controller; the invention relates to a vehicle RBF neural network design capable of being repeatedly used. The sliding-mode fault-tolerant control is designed through the powerful nonlinear approximation capability of the RBF neural network, and the stable tracking control of the reusable vehicle is realized by considering the fault-tolerant compensation of the sensor and the actuator. The method is a coupling fault-tolerant control method for the flying-pushing missile of the reusable launch vehicle, and is wide in application prospect.