Backpropagation with Vector Chaotic Learning Rate

In Neural Network (NN) training, local minimum is an integrated problem. In this paper, a modification of standard backpropagation (BP) algorithm, called backpropagation with vector chaotic learning rate (BPVL) is proposed to improve the performance of NNs. BPVL method generates a chaotic time series as Vector form of Mackey Glass and logistic map. A rescaled version of these series is used as learning rate (LR). In BP training the weights of NN become inactive, after arrival of local minima in the training session. Using integrated chaotic learning rate, the weight update accelerated in the local minimum region. BPVL is tested on six real world benchmark classification problems such as breast cancer, diabetes, heart disease, australian credit card, horse and glass. The proposed BPVL outperforms the existing BP and BPCL in terms of generalization ability and also convergence rate.