Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
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Air pollution time series is often characterized as chaotic in nature. The prediction using traditional statistical techniques and artificial neural network with back-propagation (BP) algorithm, which is most widely applied, do not give reliable prediction results. The new algorithm is therefore proposed to predict the chaotic time series based on the generalized Chebyshev neural network technique. In addition, the new algorithm has no problems such as local minima, slow convergence arising from the steepest descent-like algorithm. Finally, to illustrate the power of the Chebyshev Neural Network (CNN), a simulation example is presented to show good performance that extracts useful information from the weight functions for understanding relations inherent in the given patterns, and the trained CNN has good performance both on generalization and calculating precision.