MATLAB Program for Short-Term Load Forecasting Based on Wavelet Neural Network
MATLAB implementation of short-term load forecasting using wavelet neural network approach
Implementation of Wavelet Neural Network in MATLAB Code
A self-developed MATLAB implementation of wavelet neural network that has been successfully applied in practical scenarios, featuring optimized algorithms for complex data processing.
Time Series Prediction Using Wavelet Neural Networks
Time series prediction implementation using wavelet neural networks with MATLAB code
Wavelet Neural Networks: Integrating Wavelet Analysis with Neural Network Architectures
Wavelet Neural Networks combine wavelet analysis with neural networks by replacing traditional activation functions with wavelet basis functions, creating hybrid models for improved signal processing capabilities.
Wavelet Neural Network Implementation in MATLAB 6.5
This MATLAB 6.5 implementation of wavelet neural networks contains several areas for improvement. We welcome collaborative research and development to enhance its functionality and performance.
Network Anomaly Detection Using Wavelet Neural Networks Trained with Quantum-Behaved Particle Swarm Optimization
For network anomaly detection to improve detection rates for anomalous states and reduce false positives for normal states, this paper proposes a novel method that employs Quantum-behaved Particle Swarm Optimization (QPSO) to train Wavelet Neural Networks (WNN). The parameter set of the WNN is treated as a particle in the optimization algorithm, searching the global space for the parameter vector with the optimal fitness value. Key implementation involves encoding WNN parameters as particle positions and updating them using quantum behavior principles for enhanced convergence.
Time Series Prediction Using MATLAB Wavelet Neural Networks
Time series refers to a sequence of data points arranged at specific time intervals, representing various metrics such as product demand, production volume, or sales figures. The intervals can be measured in any time unit (hours, days, weeks, months). When establishing relationships with dependent variables proves difficult or data collection is challenging, regression analysis may not be suitable. For cases where high prediction accuracy isn't critical, time series analysis offers an effective alternative. Implementation typically involves preprocessing data using wavelet decomposition (e.g., MATLAB's wavedec function) to extract features, followed by neural network training with functions like feedforwardnet for pattern recognition and forecasting.
Solving Face Recognition Problems Using Wavelet Neural Networks
Wavelet Neural Networks offer an effective solution for face recognition with excellent practical performance. This method combines wavelet analysis with neural network training to extract discriminative facial features.
Wavelet Neural Network for Classification and Recognition
Wavelet Neural Network for classification and recognition tasks with customizable learning rate factor, network momentum factor, multi-resolution levels, and translation parameters. This example features 5 input nodes and 5 output nodes, capable of recognizing five distinct signal types. Implementation includes configurable network architecture and adjustable hyperparameters for optimal performance.
Wavelet Neural Network Prediction Algorithm
A prediction algorithm based on wavelet neural networks, implemented and validated in MATLAB with high computational efficiency.