Abstract & Overview
The calculation of biases and weights in neural network are being calculated or trained by using stochastic random descent method at the layers of the neural networks. For increasing efficiency and performance, the perturbation scheme is introduced for fine tuning of these calculations. It is aimed at introducing the perturbation techniques into training of artificial neural networks. Perturbation methods are for obtaining approximate solutions with a small parameter ε. The perturbation technique could be used in several combination with other training methods for minimization of data used, training time and energy. The introduced perturbation parameter ε can be selected due nature of training of the data. The determination of ε can be found through several trials. The application of the stochastic random descent method will increase training time and energy. The proper combined use with the perturbation will shorten training time. There exists abundance of usage of both methods, however the combined use will lead optimal solutions. A proper cost function can be used for optimum use of the perturbation parameter ε. The shortening the training time will lead determination of dominant inputs of the out values. One of the essential problems of training is the energy consuming will be decreased by using hybrid training methods.