7 results about "Wavelet neural network" patented technology

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.

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.

The application discloses a milk product detection method and device based on near-infrared spectroscopy, equipment and medium, and belongs to the technical field of food detection. The method comprises the following steps: optimizing the next generation population evolved by a genetic algorithm GA based on a snake optimization algorithm SO to obtain an optimized next generation population; wherein the position of each individual in the next generation population is a set of network parameters of a wavelet neural network WNN; obtaining an optimal individual based on the optimized next generation population; obtaining a detection model based on the optimal individual, wherein the detection model is constructed based on the WNN; in response to the input of feature data of a milk product to be detected obtained by near-infrared spectroscopy, calling the detection model to obtain a detection result. The application greatly improves the convergence speed and also improves the recognition accuracy of the detection model.

A non-contact method for monitoring the icing state of wind turbine blades includes: acquiring wind turbine images; conducting noise tests on the wind turbine rotor composed of iced blades to obtain the wind turbine noise signal under icing conditions; processing the wind turbine noise signal using an eigenvalue-optimized beamforming algorithm to obtain sound pressure level spectral characteristics; acquiring noise sources based on the wind turbine images and identifying the coordinates of the maximum noise source, establishing the relationship between the noise source and different icing states in non-icing locations; constructing a positive relationship between icing states at different locations, with different masses, and with different incoming flow angles of attack, and the increase in the total sound pressure level of the wind turbine; inputting the positive relationship into a BP neural network model and a local linear wavelet neural network model respectively to obtain monitoring results. This invention utilizes acoustic array technology to achieve non-contact measurement of horizontal axis iced wind turbine noise and thus monitor its icing state, realizing the monitoring of wind turbine blade noise under different icing states.