Wavelet Neural Network for Classification and Recognition

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Resource Overview

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.

Detailed Documentation

In this example, you can customize the wavelet neural network's classification and recognition parameters including the learning rate factor, network momentum factor, multi-resolution levels, and translation parameters. The network architecture is predefined with 5 input nodes and 5 output nodes, designed to recognize five different signal types. The implementation allows for further adjustments and optimization to enhance performance and accuracy. Key features include parameter tuning through configuration files or GUI interfaces, with the core algorithm leveraging wavelet transform for feature extraction and neural network layers for classification. Users can modify network weights through backpropagation with adjustable learning rates and momentum factors to improve convergence speed and stability.

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