IMPROVING THE ACCURACY OF GRADIENT DESCENT BACK PROPAGATIONALGORITHM (GDAM) ON CLASSIFICATION PROBLEMS
Muhammad Zubair Rehman, Nazri Mohd Nawi · 2011
The traditional Back-propagation Neural Network (BPNN) Algorithm is widely used in solving many real time problems in world. But BPNN possesses a problem of slow convergence and convergence to local minima. Previously, several modifications are suggested to improve the convergence rate of Gradient Descent Back-propagation algorithm such as careful selection of initial weights and biases, learning rate, momentum, network topology, activation function and ‘gain ’ value in the activation function. This research proposed an algorithm for improving the current working performance of Back-propagation algorithm by adaptively changing the momentum value and at the same time keeping the ‘gain ’ parameter fixed for all nodes in the neural network. The performance of the proposed method known as ‘Gradient Descent Method with Adaptive Momentum (GDAM) ’ is compared with the performances of ‘Gradient Descent Method