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Neural networks have been widely used in nonlinear time series prediction. They have generated lot of interest due to their comprehensive adaptive and learning abilities. Neural networks have been used in Medical forecasting, Exchange rate forecasting, stock index prediction, and other areas, which show a practical value of neural networks. This paper presents a novel application of the Self-organised...
This paper presents a novel application of the self-organised multilayer perceptrons inspired by the immune algorithm in financial time series prediction. The simulation results were compared with the multilayer perceptrons and the functional link neural networks. The prediction capability of the various neural networks was tested on ten different data sets; the US/UK exchange rates, the JP/US exchange...
Application of fractional derivative in control problems such as sliding surface design in sliding mode control, training of MLP in neural networks, and parameters updating in model reference adaptive control is studied in this paper. Use of the fractional derivative increases possibility of improving the control performance by reducing the convergence time in the mentioned control problems. This...
Artificial neural networks and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A family of fuzzy flip-flops is proposed, based on an artificial neural network-like structure which is suitable for approximating many-input one-output nonlinear functions. The neurons in the multilayer perceptron networks typically employ sigmoidal activation functions...
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