MATLAB Implementation of Backpropagation Neural Network
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Resource Overview
Source code for Backpropagation Neural Network implemented using MATLAB .M files, featuring detailed algorithm implementation with network initialization, forward propagation, error calculation, and weight updates through gradient descent.
Detailed Documentation
This implementation provides a comprehensive MATLAB-based Backpropagation Neural Network solution. The algorithm handles both classification and regression problems through multilayer perceptron architecture. The code structure includes network initialization with customizable layers and neurons, sigmoid activation functions, forward propagation computation, mean squared error calculation, and backward propagation with gradient descent optimization for weight adjustments. Key functions implement the core BP algorithm with configurable learning rates and iteration parameters. Users can modify hidden layer configurations, activation functions, and training parameters to adapt the network for specific applications. The .M files contain complete implementation details with commented sections explaining each computational step, making it suitable for educational purposes and practical modifications.
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