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Based on the steepest descent method, back-propagation neural networks (BPNNs) minimize an energy function for errors occurring between desired and actual outputs. Therefore, conventional BPNNs obtain local optimum weights. Stochastic search optimization methods, such as genetic algorithms, particle swarm optimization methods and artificial immune system (AIS) algorithms, have been extensively used...
Optimization problems of a back-propagation neural network (BPNN) can be categorized into optimal network topology (including the number of neurons in a hidden layer, learning rate and the momentum term) and weights. This study focuses on the optimization of weights. The conventional BPNN uses the steepest descent method, i.e. a local optimization technique, to minimize an energy function (cost function)...
In this study, an independent component analysis (ICA)-based signal reconstruction with neural network is proposed for financial time series forecasting. ICA is a novel statistical signal processing technique that was originally proposed to find the latent source signals from observed mixture signal without knowing any prior knowledge of the mixing mechanism. The proposed approach first uses ICA on...
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