Training BP Neural Networks Using Momentum Gradient Descent Algorithm

In this article, we employ the momentum gradient descent algorithm to train Backpropagation (BP) neural networks. This algorithm accelerates convergence and enhances network performance by incorporating a momentum term that considers the direction and magnitude of updates from previous iterations. The momentum mechanism helps the network escape local minima more effectively and locate global optimal solutions. The implementation typically involves storing previous weight updates and combining them with current gradients using a momentum coefficient (usually denoted as β, ranging between 0.9-0.99). Additionally, the algorithm allows balancing convergence speed and stability through adjustment of the momentum parameter. For large datasets or scenarios requiring faster BP network training, momentum gradient descent presents an optimal choice. Code implementation would involve maintaining velocity variables for each parameter and updating weights using: v = β*v + (1-β)*∇J(θ) and θ = θ - α*v, where α represents learning rate and ∇J(θ) denotes gradient.