Research on fault diagnosis and positioning of the distribution network (DN) has always been an important research direction related to power supply safety performance. The back propagation neural (BPNN) is a commonly used intelligent algorithm for location in DN. To improve accuracy dual DN, this study optimizes BPNN by combining genetic (GA) cloud theory. two types before after optimization a...

This paper presents the use of times series AutoRegressive Integrated Moving Average ARIMA(p,d,q) model with interventions, and neural network back-propagation model in analyzing the behavior of sales in a medium size enterprise located in Rio Grande do Sul Brazil for the period January 1984 – December 2000. The forecasts obtained using the neural network back-propagation model were found to be...

In this paper, we compare the forecasting accuracy of two neural network models in forecasting earnings per share of Chinese listed companies based upon fundamental accounting variables. In one neural network model, weights estimated by back propagation were utilised, and in the other model a genetic algorithm was utilised. Based upon a sample of 723 Chinese companies in 22 industries over a te...

ABSTRACT Introduction High-intensity rehabilitation training will produce exercise fatigue. Objective A backpropagation (BP) network neural algorithm is proposed to predict sports fatigue based on electromyography (EMG) signal images. Methods The principal component analysis used reduce the dimension of EMG features. knee joint angle estimated by regularized over-limit learning machine and BP a...