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Proceedings Paper

The combination of immune evolution and neural network for nonlinear time series forecasting
Author(s): Xiulan Wen; Xiaolan Xue; Peng Zhang; Jing Tan
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Paper Abstract

Forecasting and dynamic modeling have common applications in science and engineering. Time series data are often found from different sources, astrophysical, biological, economical, etc. It is then very important to predict future values of these data series from the existing data. The immune system is a very complex system with several mechanisms to defense against pathogenic organisms. Inspired by the principles of immune system and biology evolution, a novel algorithm based on the combination of immune evolution and neural network is proposed to forecast nonlinear time series, which imitates the cellular clonal selection theory of biology immune system and the mutation ideas of biology evolution process. Then, the mutation intensity of each antibody is decided by its objective function value; similar antibodies are suppressed by computing the affinity of antibodies and new antibodies are produced dynamically to maintain the diversity. Application of the proposed algorithm to nonlinear time series of sunspots number modelling and prediction is investigated. The experimental results by different methods confirm that the proposed method has better generalization performance than that of the Fuzzy genetic algorithm (FGA), Genetic programming (GP), Automatic Regression Model (AR) and Automatic Regression Moving Average Model (ARMA)

Paper Details

Date Published: 28 October 2006
PDF: 5 pages
Proc. SPIE 6358, Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation, 635805 (28 October 2006); doi: 10.1117/12.717498
Show Author Affiliations
Xiulan Wen, Inner Mongolia Polytechnic Univ. (China)
Xiaolan Xue, Inner Mongolia Polytechnic Univ. (China)
Peng Zhang, Inner Mongolia Polytechnic Univ. (China)
Jing Tan, Inner Mongolia Polytechnic Univ. (China)


Published in SPIE Proceedings Vol. 6358:
Sixth International Symposium on Instrumentation and Control Technology: Sensors, Automatic Measurement, Control, and Computer Simulation
Jiancheng Fang; Zhongyu Wang, Editor(s)

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