BP Neural Network Adaptive Learning Rate Training Algorithm

The original text introduces the BP Neural Network Adaptive Learning Rate Training Algorithm, which employs both the minimum error method and gradient descent approach while incorporating adaptive weight adjustment capabilities. This algorithm enhances the network model's adaptability to diverse datasets and optimizes model performance. The gradient descent method iteratively updates weights through backpropagation to minimize the error function, typically implemented using derivative calculations of activation functions like sigmoid or ReLU. The adaptive weight adjustment mechanism dynamically modifies the learning rate based on current error conditions, which can be programmed using techniques like momentum-based updates or gradient magnitude monitoring. This intelligent step-size regulation improves training efficiency by preventing oscillations in steep regions and accelerates convergence in flat areas of the error surface, ultimately enhancing both training speed and prediction accuracy.