19 results about "Wavelet neural network" patented technology

The invention provides a gait evaluation method and system based on a human body nonlinear system analysis technology, and the method comprises the steps: collecting a gait cycle six-channel high-precision time sequence signal outputted by a wearable inertial measurement unit, eliminating noise and gait difference through wavelet threshold denoising and Z-score standardization, extracting a chaotic feature vector composed of a Lyapunov index spectrum and a Kolmogorov entropy value, and carrying out the recognition of a gait signal, a wavelet neural network is adopted to realize nonlinear mapping of chaotic features and phase-space reconstruction parameters, and a self-adaptive feedback mechanism is introduced to dynamically optimize modeling parameters, so that the accuracy and personalized matching capability of gait pattern recognition and stability evaluation are effectively improved; quantitative characterization of the gait chaos level and real-time online model optimization can be achieved, and high-robustness support is provided for rehabilitation training and exercise aided decision making.

The present application relates to a kind of discrete multi-motor servo system fuzzy wavelet neural network control method with event triggering mechanism, belong to multi-motor servo system control field, comprising the following steps: S1: the discrete time system model of multi-motor servo system is established;S2: design two type fuzzy wavelet neural network to estimate unknown nonlinear function caused by external interference and internal parameter perturbation;S3: based on the backstepping control framework, introduce event triggering mechanism with dead zone operator, design discrete time fuzzy wavelet neural network controller;S4: using the discrete time fuzzy wavelet neural network controller, control multi-motor servo system is carried out.

The present application relates to a cross-stitch network-based tool multi-task high-synergy monitoring method, comprising the following steps: S1, collecting tool vibration signals, constructing a training data set and a test data set; S2, using a wavelet neural network for feature extraction for the anomaly detection task, and using a gated recurrent network for feature extraction for the RUL prediction task; S3, constructing a cross-stitch network, performing deep feature extraction and shunt-type feature fusion; S4, constructing a regularization loss function, inputting the training data set, and using an automatic weight adjustment strategy to adaptively optimize the loss weights of different tasks, so that the regularization loss function reaches a minimum, and the network training is completed; S5, inputting the test data set into the trained network model to obtain the results of the anomaly detection task and the RUL prediction task. The present application innovatively introduces and optimizes and improves the cross-stitch network and applies it to tool multi-task monitoring, significantly improves the tool multi-task synergy, can achieve higher classification and prediction accuracy, and has good stability.

The invention provides an adaptive equalization method for a PON, and the method comprises the steps: decomposing an input unbalanced signal into a preset number of layers through employing a Mallat decomposition algorithm, and obtaining a high-frequency detail coefficient and a low-frequency approximation coefficient of each layer; performing denoising processing on the high-frequency detail coefficient of each layer: calculating a GCV score of the high-frequency detail coefficient of the current layer by using a generalized cross validation threshold method, determining an optimal denoising threshold value for denoising processing based on the GCV score, and performing denoising processing on the high-frequency detail coefficient based on the optimal denoising threshold value by using a nonlinear contraction function; based on the denoised high-frequency detail coefficient of each layer and the denoised low-frequency approximation coefficient of the corresponding layer, performing layer-by-layer iterative reconstruction by using a Mallat reconstruction algorithm to obtain a denoised data symbol sequence; and taking the denoised data symbol sequence as the input of a pre-trained embedded wavelet neural network, and outputting an equalized signal.

The invention relates to a converter end point carbon content prediction method based on a wavelet neural network, which comprises the following steps: firstly, carrying out data preprocessing on original data, sequentially finishing blank value elimination, abnormal value screening and normalization processing, then screening out weak correlation process parameters through Pearson's correlation coefficient analysis in combination with a metallurgy mechanism, and finally, carrying out prediction on the end point carbon content of a converter. Subsequently, dimensionality reduction is carried out through principal component analysis, and finally an optimized input parameter set is formed to replace original full-amount parameters. After the optimization parameter set is substituted into the wavelet neural network model, the model complexity is greatly reduced, and the training efficiency is improved. And finally, inputting production data into the wavelet neural network prediction model to obtain an end point carbon content prediction result. According to the method, modeling is carried out based on the wavelet neural network, deep learning driving replaces traditional mechanism driving, complex reactions and nonlinear relations in automatic learning data can be achieved, and the prediction precision of the converter end point carbon content is improved.

The invention relates to a wavelet neural network dynamic construction method, which comprises the following steps of: setting a nonlinear system, and processing an unknown nonlinear function in the nonlinear system by adopting a self-adaptive scaling wavelet network approximation framework; processing the non-linear function, namely determining an initial wavelet space for approaching the non-linear function by utilizing an initial wavelet frequency estimator of the adaptive scaling wavelet network approaching framework; on the basis of a scaling parameter self-adaptive adjustment mechanism, scaling parameters are dynamically updated on line so as to adjust the frequency bandwidth of the wavelet basis function and enable the frequency bandwidth to be matched with the spectrum distribution of the nonlinear function; and based on a wavelet basis function increasing and cutting mechanism of the adaptive scaling wavelet network approximation framework, increasing and cutting a wavelet basis function on the basis of the initial wavelet space so as to further approach the nonlinear function.

The application relates to a UPFC device fault prediction method based on a wavelet neural network, and comprises the following steps: defining a fault variable of a unified power flow controller; classifying the fault variable, and defining a data acquisition structure for device state monitoring and fault prediction; acquiring signal data of each node of the unified power flow controller, classifying and coding the signal data after filtering and A / D conversion; constructing a device fault prediction model by adopting a wavelet neural network; inputting real-time signal data into the trained device fault prediction model to obtain a device state prediction result output by the device fault prediction model; judging whether a device fault exists according to the device state prediction result; and issuing a fault early warning signal for the predicted device fault. The application realizes multi-dimensional and all-around UPFC online state monitoring, fault diagnosis and fault early warning, predicts specific fault information of the UPFC device in advance, effectively protects the operation safety of the device, and avoids damage to other power electronic elements caused by the UPFC device fault.

The invention relates to the technical field of electric power data processing, in particular to an electric energy metering data anomaly detection method, system and device and a medium, and the method comprises the steps: carrying out the moving average filtering and standardization processing of original electric energy metering data, and generating preprocessing data; dynamically selecting a target wavelet basis function according to the spectrum kurtosis value of the preprocessed data, performing multilayer wavelet packet decomposition on the preprocessed data, and extracting a high-frequency detail component and a low-frequency approximate component; energy class features of the high-frequency detail components and statistical features of the low-frequency approximate components are extracted, fusion and normalization are carried out, and multi-dimensional feature vectors are constructed; inputting the multi-dimensional feature vector into a wavelet neural network model, and outputting an abnormal probability value of the current time point; and comparing the abnormal probability value of the current time point with a dynamic threshold value to judge whether the current time point is an abnormal point or not. The method has the beneficial effects that the accuracy, the stability and the self-adaptive capability of the abnormal detection of the electric energy metering data are remarkably improved.

The invention discloses a dissolved oxygen concentration control method based on a pipeline recursive wavelet neural network, and relates to the field of artificial intelligence. Firstly, a control factor of the DO concentration is analyzed based on a reaction mechanism; and then, constructing a pipeline recursive wavelet neural network to control the DO concentration. Meanwhile, an improved adaptive learning rate algorithm is adopted to train network parameters. Therefore, the DO control precision in the sewage treatment process is improved.

This invention discloses a method and device for early warning of energy consumption in air conditioning refrigeration systems based on genetic algorithms. It aims to solve the problems of traditional energy consumption prediction models, such as single feature set, weak nonlinear modeling ability, reliance on experience for parameter tuning, and susceptibility to local optima. The method first integrates operating parameters such as indoor and outdoor temperature difference, humidity, compressor performance, and refrigerant charge to construct input features that comprehensively reflect the system state. Second, it designs a wavelet neural network with Morlet wavelet function as the activation function and introduces a genetic algorithm to globally optimize network weights, scaling factors, and translation factors, automatically searching for the optimal parameter combination to improve model convergence speed and prediction accuracy. Third, it dynamically sets energy consumption thresholds based on historical data, compares the prediction results in real time, and triggers an early warning when the threshold is exceeded. This method ensures high accuracy while possessing good generalization ability and engineering applicability, providing effective support for intelligent operation and maintenance and energy-saving management.

This invention discloses a method for classifying coral reef substrate based on the fusion of lidar and side-scan sonar data, mainly including the following steps: (1) By analyzing the structure of the photon-counting lidar scanning system, a precise calculation model for the coordinates of underwater depth sounding points of UAV-borne lidar is constructed; (2) Through fine processing steps such as seabed line tracking and joint correction of radiation distortion, a geocoded side-scan sonar image is constructed; (3) The backscattering intensity of lidar and side-scan sonar is fused to construct a seabed topographic image; (4) The standard wavelet BP neural network is improved; (5) Based on the fused image, coral reef species samples are extracted, and feature parameters with high self-cohesion and resolution are selected for sample training. The sample training results are used to classify the coral reef substrate in the fused image. Through the above steps, coral reef substrate classification based on the fusion of lidar and side-scan sonar data can be realized, providing technical support for identifying coral reef types.

The application relates to the technical field of intelligent equipment manufacturing, and specifically discloses an AI-based equipment manufacturing process energy consumption optimization and carbon footprint tracking system, which comprises a central collaborative scheduler, and the central collaborative scheduler is communicatively connected with the following modules: a data self-checking reconstruction module, a wavelet neural network optimized by using an improved particle swarm algorithm is used to construct a data self-checking model, and time sequence analysis and correlation comparison are carried out on multi-source heterogeneous sensing data of an equipment manufacturing process; the application can dynamically identify and correct inherent errors of sensing data by deploying a multi-source heterogeneous sensor network, combining a precise time synchronization protocol, constructing a time sequence alignment sequence, using a wavelet neural network optimized by using an improved particle swarm algorithm to construct a data self-checking model, outputting a high-fidelity data stream, and solving the problem of insufficient reliability of carbon footprint accounting data in a traditional manufacturing process from the source, thereby providing a data basis for energy consumption optimization and carbon emission tracking, and ensuring the authenticity and effectiveness of accounting results.