In training artificial neural networks, Backpropagation has been frequently used and known to provide powerful tools for classification. Due to its capability to model linear and non-linear systems, it is widely applied to various areas, offering solutions and help to human experts. However, BP still has shortcomings and a lot of studies had already been done to overcome it. But one of the important elements of BP, the stopping criterion, was given a little attention. The Fisher's Iris data set was used to this study as input for standard BP. Three experiments, using the different training set sizes, were conducted to measure the effectiveness of the proposed stopping criterion. The accuracy of the networks, trained in different data set sizes were also tested by using the corresponding testing sets. The experiments have shown that the proposed stopping criterion enabled the network to recognize its minimum acceptable error rate allowing it to learn to its maximum potential based on the presented patterns. The ubiquitous stopping criterion presented in this paper proved that the number of iterations to train the network should not be dictated by human since the accuracy of the network depends heavily on the number and quality of the training data.

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Abstract. In this paper, the monthly consumption of the 50 states in USA was used to train the adaptive multilayer perceptron (AMLP), and to validate and test the accuracy of the produced models. The integration of different adaptive algorithms, such as adaptive stopping criterion, autocorrelation, and adaptive weight initialization, in the feedforward neural networks trained with backpropagation has significantly decreased the training time, while increasing the accuracy of the model in forecasting the monthly electric load consumption.

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