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We give a novel neuron model whose activation function have two independent variables, one is the conventional input, and the other is its change rate. The response of the neuron has the hysteretic characteristics. And the output of the neuron is related to not only the current input but also the history input, which makes the neuron have stronger memory ability. The neural network composed by the...
A neural networks are able to give solutions to complex problems in business intelligence and financial engineering due to their nonlinear processing. This paper consists of a survey of various business intelligence and financial engineering and so on applications based on the neural networks, and also a summary of the recent techniques such as still evolutionary algorithms, cellular computing, Bayesian...
In this paper, a good points set-evolutionary strategy (GPSES) is applied to solve the problem of tuning both network structure and parameters of a feedforward neural network. Good point set (GPS) is a concept in number theory. To overcome the deficiency of orthogonal design to handle optimization problems, this paper presents a method that incorporate GPS principle to enhance the crossover operator...
In order to avoid the over-fitting in the training of neural networks, we apply Bayesian learning to neural networks. We illustrate the advantages of Bayesian learning by concentrating on multilayer perceptrons (MLP) neural networks and Markov Chain Monte Carlo (MCMC) method for computing the integrations. We conduct the experiments on the foreign exchange rate forecasting by using the approach. The...
Deregulation has created a competitive market among power market participants, and the pricing system plays an important role. Locational marginal pricing (LMP) provides clear market signals that identify the locations where power market participants could make their decisions so as to maximize their profits. In this work, artificial neural networks (ANNs) models are used to predict hourly LMP. ANN...
In the presented paper by analyzing the curve of the daily electrical network load in Iran over a 10 year period; the effective factors on the daily electricity consumption (including time, environmental and special factors) are studied. Additionally , using the final results from this graphical analysis, a suitable method to train artificial neural networks for short-term forecasting of the time...
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