Dynamic modelling and time-series prediction by incremental growth of lateral delay neural networks

Chan, L. and Li, Y. (2000) Dynamic modelling and time-series prediction by incremental growth of lateral delay neural networks. In: IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, San Antonio, TX, 11-13 May 2000, pp. 216-223. (doi:10.1109/ECNN.2000.886237)

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Publisher's URL: http://dx.doi.org/10.1109/ECNN.2000.886237

Abstract

The difficult problems of predicting chaotic time series and modelling chaotic systems is approached using an innovative neural network design. By combining evolutionary techniques with others, good results can be obtained swiftly via incremental network growing. The network architecture and training algorithm make the creation of dynamic models efficient and hassle-free. The network results accurately reflect the outputs of the chaotic systems being modelled and preserve complex attractor structures of these systems.

Item Type:Conference Proceedings
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Chan, L., and Li, Y.
Subjects:Q Science > Q Science (General)
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy

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