Backpropagation Neural Network Algorithm Implementation in C++

This C++ developed backpropagation neural network algorithm provides an in-depth understanding of the BP algorithm's architecture. As a supervised learning method based on artificial neural networks, it trains models to adapt to various input and output data patterns. The core mechanism of the BP algorithm involves adjusting network weights and biases through error backpropagation to enhance model accuracy and performance. The implementation follows a structured approach: input data is propagated through the input layer, processed through multiple hidden layers using activation functions (typically sigmoid or ReLU), and finally produces results at the output layer. During this forward propagation phase, each neuron calculates its output using weighted sums and activation functions. The key innovation lies in the backward propagation phase: the algorithm computes the error between actual outputs and expected targets using loss functions like Mean Squared Error. This error gradient is then propagated backwards through the network using chain rule differentiation. The code implements weight updates through gradient descent optimization, where weights and biases are adjusted proportionally to their contribution to the final error. Critical code components include: - Network initialization with randomized weights - Forward propagation with matrix operations - Error calculation and backpropagation logic - Weight update procedures using learning rate parameters - Iterative training cycles with convergence checking Through iterative learning and systematic weight adjustments, the BP algorithm enables the network to progressively approximate desired output patterns, achieving effective model training and prediction capabilities. The C++ implementation emphasizes computational efficiency through optimized matrix operations and memory management.