16 results about "Radial basis function network" patented technology

The invention relates to the technical field of industrial automation control, and discloses a mixed PID temperature control system and method based on GDNN and multi-modal prediction. The system comprises a data acquisition module, an enhanced GDNN core controller, a prediction optimization module and an actuator module. And real-time adaptive tuning of PID parameters (Kp, Ki and Kd) is realized by fusing a general dynamic neural network, a genetic algorithm optimization initial weight, a variable learning rate and an additional momentum method, and a prediction model module of a multi-layer perceptron (MLP) and a radial basis function (RBF) network. According to the system, nonlinear, time-varying and interference factors can be processed for temperature control scenes such as a high-temperature furnace and a drying system, the mean absolute error, overshoot and stabilization time are remarkably reduced, and the energy efficiency is improved. A multi-modal sensor fusion and edge computing framework is innovatively introduced, and distributed industrial application is supported. The system is suitable for the fields of high-temperature furnaces, drying systems, intelligent buildings and the like, and provides high robustness and interpretability.

Embodiments of the invention provide a control method and apparatus, and an electronic device. The method comprises the steps of determining current information of a Stewart platform; the current information comprises a current pose and the rod length of each rod; inputting the current information into a prediction model to obtain adjustment information of the Stewart platform; wherein the prediction model is a network model obtained by optimizing a radial basis function network through a particle swarm optimization algorithm; control information of the Stewart platform is determined according to the adjustment information; and adjusting the current information according to the control information.

The invention discloses an SAR (Synthetic Aperture Radar) image geometric correction method and system for optimizing a radial basis function neural network based on a genetic algorithm. The method comprises the following steps: normalizing three-dimensional geographic coordinates of ground feature points, and inputting the normalized three-dimensional geographic coordinates to a full connection layer for feature extraction; constructing a ReRBF network, wherein the ReRBF network is formed by stacking a plurality of layers of RBF sub-modules through a residual connection structure; and the output of the previous module is used as a residual error, is spliced with the original input or the intermediate feature, and is jointly used as the input of the next module to form a progressive feature enhancement and residual error learning mechanism, so that the network can refine the coordinate mapping relationship layer by layer. And a DBSCAN algorithm is adopted to perform automatic clustering on training data, so that the generalization ability of the model in multiple scenes is improved. And optimizing the ReRBF network by using a genetic algorithm, and adaptively determining the optimal width parameter of the radial basis function network. According to the method, the geometric positioning precision and the processing efficiency of the SAR image under the complex terrain condition are improved.

A method for controlling a torque of at least one wheel of a mobile platform. The method includes: providing at least one current slip value of the wheel and at least one current wheel acceleration of the wheel as input values; providing a trained radial basis function network designed to determine, by means of the input values, at least one torque change as an output value for control of the at least one wheel; and determining a current torque change, by means of the trained radial basis function network and the provided input values, for control of the torque.

The application discloses a kind of intelligent sports movement state analysis method, system, equipment and medium based on multi-module collaborative optimization.Method includes: using lightweight real-time model to video stream is human detection and initial key point positioning;Through radial basis function network, initial key point is nonlinear fine-tuning to improve accuracy;From the key point sequence after fine-tuning, extract high-dimensional kinematic feature, and utilize principal component analysis to reduce dimension, obtain core low-dimensional time sequence feature;Finally, through time sequence neural network, feature sequence is modeled, realizes action recognition and posture evaluation.The application passes through the collaborative optimization link of "perception-fine-purification-understanding", effectively solves the problems, such as imbalance of accuracy and speed, feature redundancy, time sequence modeling deficiency and error accumulation, in prior art, provides efficient, accurate, automated analysis solution for wisdom sports training.

The application discloses a kind of cable fault prediction processing method, device and electronic equipment.Therein, the method comprises: obtaining the partial discharge characteristic data of high-voltage cable;First training set data in partial discharge characteristic data is respectively input to initial radial basis function network model and initial long short-term memory network model for training;First test set data in partial discharge characteristic data is respectively input to the radial basis function network model after training and the long short-term memory network model after training, obtain the first output result output by the radial basis function network model after training, and the second output result output by the long short-term memory network model after training;Based on first output result and second output result, determine cable fault prediction model.The present application solves the technical problems of low cable fault prediction efficiency and poor prediction accuracy caused by poor performance of cable fault prediction model in related art.

The embodiment of the application provides a three-dimensional induction welding coil optimization design method and related equipment, and belongs to the field of advanced manufacturing and electromagnetic heating technology. The method comprises the following steps: discretizing a three-dimensional design space, defining geometric and topological constraints, and constructing a scalarization objective function; according to the obstacle layout, the homotopy class is divided, the representative curve is generated as an initial topological template; a differential homeomorphism mapping parameterized by a radial basis function network is constructed to drive the representative curve to deform geometrically; a serialization decision strategy is adopted, a fast proxy model based on the Biot-Savart law and a local gradient adaptive step are combined to iteratively optimize the deformation parameters at low cost; the candidate path is verified by high-fidelity finite element simulation, and the design meeting the engineering index and having the maximum and minimum performance margin is selected as the final result. The application significantly reduces the calculation cost while strictly guaranteeing the physical feasibility of the coil, and improves the efficiency and yield of complex structure design.

A method for reconstructing images, an electronic device and a storage medium is provided. In the method, the electronic device obtains an image to be reconstructed of an object captured by a target camera device, and obtains a sample image of the object. A radial basis function network is trained based on the image to be reconstructed and the sample image. An image reconstruction model is obtained by converting the radial basis function network based on a deconvolution algorithm, the image reconstruction model is configured for reconstructing images captured by the target camera device. By performing the method, a clarity of the image to be reconstructed can be improved.

This invention relates to the field of partial discharge pattern recognition technology, specifically to a partial discharge pattern recognition method based on adaptive mathematical morphology. The method first acquires partial discharge signal data from defective equipment and plots PRPD (partial discharge pattern recognition) maps. After grayscale and binarization processing, binary discharge images are obtained, and a dataset is constructed. Then, adaptive mathematical morphology parameter optimization is performed on the binary discharge images in the training set. The optimal results are applied to extract morphological change features and multi-dimensional morphological structure features from the binary discharge images in the entire dataset. Finally, the extracted features are weighted and fused, input into a radial basis function network for pattern recognition, and the recognition result is output. This invention effectively improves the accuracy of partial discharge pattern recognition through adaptive morphological parameter optimization and multi-dimensional feature fusion, and is suitable for insulation condition monitoring and fault diagnosis of power equipment.

This invention relates to the field of materials data modeling technology, specifically to a method for constructing a thermal processing map for metallic materials. The method includes the following steps: calculating the true stress-strain sequence; fitting the true stress-strain sequence to a continuously differentiable three-dimensional rheological response surface based on a radial basis function network; performing analytical differentiation on the three-dimensional rheological response surface to extract global strain rate sensitivity field data; calculating the power dissipation efficiency matrix and rheological instability judgment parameters based on the strain rate sensitivity field data; and then generating a thermal processing map. In this invention, by establishing a continuously responding surface model based on global optimization and using analytical derivative solutions to replace the traditional differential calculation of discrete data, the numerical oscillation phenomenon caused by experimental noise is effectively eliminated, ensuring thermodynamic consistency in the constitutive relation calculation process, significantly improving the geometric accuracy of the power dissipation efficiency contour lines, and achieving accurate prediction of the safe processing window for metallic materials under complex thermal deformation conditions.

The invention provides a gas extraction evaluation unit multi-parameter collaborative automatic metering method and system. The method comprises the steps that gas concentration, multi-path mixed gas flow velocity, pipeline pressure and multi-component gas data are collected, a radial basis function network is input, gas density-viscosity correction parameters and cross interference correction parameters are output, and a flow velocity compensation coefficient is calculated; calculating a first-order difference norm and fluctuation entropy of a gas concentration sequence in a rolling time window, and judging a signal state according to a joint criterion: correcting the concentration by using a correction parameter when the signal is stable, decomposing and reducing noise by using a wavelet packet when the signal fluctuates, and uniformly outputting and correcting the gas concentration; constructing a flow velocity-pressure covariance matrix, calculating a characteristic value time change rate, generating a confidence factor in combination with a fluctuation entropy, carrying out weighted fusion on each path of flow velocity to obtain a reference mixed flow velocity, and carrying out correction by using a compensation coefficient to obtain a compensated mixed flow velocity; and aligning the time sequences of the corrected gas concentration and the compensated mixed flow velocity, and calculating the gas extraction pure quantity based on the two time sequences.

The invention relates to the technical field of calibration, in particular to a dynamic intelligent optical sighting telescope calibration method and system, and the method comprises the following steps: sorting initial data of a sighting telescope based on collected environment parameters, obtaining environment state information, carrying out the data filling of an input layer of a radial basis function network according to the environment state information, carrying out the preliminary training of a model, and carrying out the calibration of the optical sighting telescope. The weight and deviation of the model are adjusted to obtain a preliminary adjustment model; according to the method, environmental parameter analysis and real-time data adjustment are integrated, the adaptability and accuracy of the calibration process are enhanced, dynamic adjustment is performed by using environmental state information, a calibration model is allowed to be automatically optimized when external conditions change, the accuracy is ensured to be closely matched with an operation environment, and the calibration setting is automatically updated, so that the calibration efficiency is improved. The operation complexity is reduced, the automation level of the calibration process is improved, the calibration of the sighting telescope is quicker and more accurate, and the use effect of the sighting telescope in various environments is enhanced through an automatic data feedback and performance adjustment mechanism.

The invention discloses an RBF-PID (Radial Basis Function-Proportion Integration Differentiation)-based ventilation adaptive control system and method for a limited space, and the method comprises the steps: collecting the ventilation state quantity and environment index quantity of each measurement point, and carrying out the abnormality elimination and time alignment to form a synchronous sequence; constructing conservation observation according to ventilation state quantities of a main pipe inlet, an on-way and a working plane, recursively identifying an air leakage estimation quantity, and updating an effective air volume mapping model; generating a wind path topological code according to the ventilation state quantities of the two sides of the branch regulating part, estimating a branch coupling coefficient matrix on line, and generating a backward flow mark quantity according to the pressure difference symbol and the flow direction consistency; and forming an input vector by the control deviation, the air leakage estimator, the coupling coefficient matrix characteristic quantity and the backward flow marker quantity, and outputting a PID gain update quantity through a radial basis function network. Through conservation observation and topology perception driven RBF-PID cooperative control, the problems of air leakage time varying and multi-branch coupling backflow are solved, and stable, accurate and efficient adjustment of ventilation of the limited space is achieved.

The invention relates to the technical field of food sensory evaluation, in particular to a food sensory experience evaluation and personalized recommendation system based on electroencephalogram multi-modal fusion, which comprises a food stimulation module, a physiological reaction acquisition module, a computer system, a machine learning module and a personalized recommendation feedback module, according to the system, multi-modal physiological data such as electroencephalogram signals, electrocardiosignals and facial micro-expressions of a subject in the food stimulation process are collected, topological features are extracted by adopting a topological data analysis method, emotion state manifold characterization is constructed, topology preserving mapping from the emotion state to food attributes is achieved, and the food quality is improved. The core innovation of the invention lies in that a continuous coherence theory is adopted to process multi-modal physiological signals, an 8-dimensional emotional state manifold is constructed, emotional-food attribute mapping is realized through a multilayer radial basis function network, topology is designed to maintain constraints to ensure the stability of a structural relationship, and the system can objectively evaluate the sensory experience of food and improve the quality of food. The defect that a traditional evaluation method is high in subjectivity is overcome.

The application discloses a kind of multi-fan operation system, adaptive control method and device, the method includes: the operating state data of fan is collected in real time by sensor component;If the data fluctuation amplitude in operating state data is greater than first preset threshold, operating state data is processed by Kalman filtering, and first prediction data is obtained;If the data fluctuation amplitude in first prediction data is greater than second preset threshold, first prediction data is input radial basis function network model, and second prediction data is obtained;Second prediction data is sent to PID controller, and target speed in the operation process of fan is obtained by adjusting PID controller parameter through particle swarm algorithm.It can realize accurate state estimation, dynamic modeling prediction and real-time adaptive control of multi-fan system by integrating Kalman filtering technology radial basis function network model and PID controller, effectively cope with wind speed and load fluctuation and other complex working conditions, and improve the overall performance of system.

The present application relates to model adaptive control technical field, specifically for a kind of guide vane opening adaptive control method for hydroelectric generating set, including the following steps, in the present application, by adopting radial basis function network Gaussian node dynamic addition and deletion and weight optimization back mapping algorithm, guide vane relay actuator displacement instruction and actual execution stroke steady-state error absolute value is forced to converge to zero point zero five millimeter closed set, by adopting Kalman filter covariance recursive estimation and multidimensional space boundary constraint optimization algorithm, limit the speed overshoot under sudden load disturbance condition to be less than the threshold value of rated speed percentage three, to ensure that the frequency fluctuation damping ratio of output electric energy is greater than the target value of zero point two five, by adopting speed deviation feedforward multiplication deduction and steady-state threshold truncation reorganization algorithm, maintain the consistency of electrical control flow during the mutation of multiple unit operating conditions, improve the active power regulation dynamic response stability margin of hydroelectric generating set grid-connected operation.