BP Neural Network Function Approximation Source Code
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This is a comprehensive source code implementation for BP neural network function approximation! We sincerely appreciate your contribution. The program demonstrates the core backpropagation algorithm with detailed implementation of forward propagation, error calculation, and weight updates using gradient descent. Key functions include network initialization, sigmoid activation layers, and iterative training loops that enable the neural network to learn complex nonlinear mappings. This implementation provides valuable insights into how neural networks can approximate mathematical functions through supervised learning, making it an excellent resource for studying and exploring neural network applications in function approximation problems. Thank you for sharing this practical educational tool!
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