Backpropagation Algorithm for Three-Layer Feedforward Neural Network
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Resource Overview
Implementation of the Backpropagation (BP) algorithm for a three-layer feedforward neural network. The program includes the following key features: (1) Configurable node counts for each layer (input, hidden, output); (2) Adjustable learning rate η for controlling weight update speed; (3) Weight initialization with random values in the [-1, 1] range; (4) Support for both unipolar and bipolar Sigmoid activation functions.
Detailed Documentation
This implementation of the Backpropagation algorithm for a three-layer feedforward neural network provides comprehensive configuration options. The program allows users to: (1) Specify the number of nodes in each layer (input layer, hidden layer, and output layer) through parameter settings. (2) Select different learning rates (η) to control the speed of weight updates during gradient descent optimization. (3) Initialize connection weights using random numbers within the [-1, 1] interval to ensure network randomness and break symmetry. (4) Choose between unipolar (range [0,1]) and bipolar (range [-1,1]) Sigmoid activation functions for non-linear transformation, implemented through conditional function selection in the code. The algorithm follows standard BP procedures including forward propagation, error calculation, and backward weight adjustment using chain rule differentiation.
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