Abstract
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.
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© 2011 Springer-Verlag Berlin Heidelberg
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Li, F. (2011). Application of Generalized Chebyshev Neural Network in Air Quality Prediction. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6728. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21515-5_56
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DOI: https://doi.org/10.1007/978-3-642-21515-5_56
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21514-8
Online ISBN: 978-3-642-21515-5
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