Implementing Three-Layer Backpropagation Neural Network for XOR Problem Solution
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Resource Overview
MATLAB implementation of a three-layer BP artificial neural network to solve the XOR problem, featuring adjustable network architecture with configurable hidden layer neurons and activation functions
Detailed Documentation
This article demonstrates how to solve the XOR problem using a three-layer backpropagation neural network implemented in MATLAB. The implementation utilizes MATLAB's neural network toolbox, where the network architecture can be easily modified through parameters such as hidden layer size and activation function selection. Key components include the feedforward computation using sigmoid activation functions and backpropagation algorithm for weight updates. The network configuration allows adjustment of hidden layer neurons (typically 2-4 neurons for XOR) and learning rate parameters to optimize convergence. The implementation involves initializing weights randomly, performing forward propagation to calculate outputs, computing error using mean squared difference, and applying gradient descent with backpropagation to adjust weights iteratively until the network learns the XOR mapping pattern.
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