基于剪接系统的遗传算法RBF网络建模方法

CIESC Journal

• SYSTEM ENGINEERING • 上一篇下一篇

基于剪接系统的遗传算法RBF网络建模方法

陶吉利; 王宁

National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2007-04-28 发布日期:2007-04-28
通讯作者: 陶吉利

Splicing system based genetic algorithms for developing RBF networks models

TAOJili;WANGNing

National Laboratory of Industrial Control Technology, Institute of Advanced Process Control, Zhejiang University, Hangzhou 310027, China

Received:1900-01-01 Revised:1900-01-01 Online:2007-04-28 Published:2007-04-28
Contact: TAO Jili

摘要/Abstract

摘要： A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function (RBF) neural network, which is used to extract valuable process information from input output data. The novel RBF network training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity. The ef-fectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.

关键词: RBF network;structure optimization;genetic algorithm;splicing system

Abstract: A splicing system based genetic algorithm is proposed to optimize dynamical radial basis function (RBF) neural network, which is used to extract valuable process information from input output data. The novel RBF network training technique includes the network structure into the set of function centers by compromising between the conflicting requirements of reducing prediction error and simultaneously decreasing model complexity. The ef-fectiveness of the proposed method is illustrated through the development of dynamic models as a benchmark discrete example and a continuous stirred tank reactor by comparing with several different RBF network training methods.

Key words: RBF network, structure optimization, genetic algorithm, splicing system

陶吉利, 王宁. 基于剪接系统的遗传算法RBF网络建模方法[J]. CIESC Journal.

TAOJili,WANGNing. Splicing system based genetic algorithms for developing RBF networks models[J]. .

参考文献

1    Park, J., Sandberg, I., “Universal approximation using radial basis function networks”, Neural Comput., 3(2), 246—257(1991).
2    Prechelt, L., “Automatic early stopping using cross vali-dation: quantifying the criteria”, Neural Network., 11(4), 761—767(1998).
3    Alexandridis, A., Sarimveis, H., Bafas, G., “A new algo-rithm for online structure and parameter adaptation of RBF networks”, Neural Network., 16(7), 1003—1017(2003).
4    Holland, J.H., Adaptation in Natural and Artificial Sys-tems, MIT Press, Cambridge, MA, USA (1992).
5    Wang, Y., Yao, P., “Simulation and optimization for ther-mally coupled distillation using artificial neural network and genetic algorithm”, Chin. J. Chem. Eng., 11(3),  307—311(2003).
6    Yang, T., Lin, H.C., Chen, M.L., “Metamodeling ap-proach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study”, Robot Comput. Integrated Manuf., 22(4), 322—331(2006).
7    Blanco, A., Delgado, M., Pegalajar, M.C., “Real-coded genetic algorithm for training recurrent neural networks”, Neural Network., 14(1), 93—105(2001).
8    Delgado, M., Pegalajar, M.C., “A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference”, Pattern Recognit., 38(9), 1444—1456(2005).
9    Vesin, J.M., Gruter, R., “Model selection using a simplex reproduction genetic algorithm”, Signal Process., 78(3), 321—327(1999).
10    Esposito, A., Marinaro, M., Oricchio, D., Scarpetta, S., “Approximation of continuous and discontinuous map-pings by a growing neural RBF-based algorithm”, Neural Network., 13(6), 651—665(2000).
11    Sarimveis, H., Alexandridis, A., Mazarakis, S., Bafas, G., “A new algorithm for developing dynamic radial basis function neural network models based on genetic algo-rithms”, Comput. Chem. Eng., 28(1), 209—217(2004).
12    Chen, S., Cowan, C.F.N., Grant, P.M., “Orthogonal least squares learning algorithm for radial basis function net-works”, IEEE Trans. Neural Netw., 2(2), 302—309(1991).
13    Li, S.C., Xu, J., Pan, L.Q., “Operational rules for digital coding of RNA sequences based on DNA computing in high dimensional space”, Bull. Sci. Tech. Soc., 18(6),  388—398(2003).
14    Jonoska, N., Seeman, N.C., DNA Computing, Springer-Verlag, New York (2002).
15    Pham, D.T., Dimov, S.S., Nguyen, C.D., “Selection of K in k-means clustering”, Proc. Inst. Mech. Eng. C. Mech. Eng. Sci., 219(1), 103—119(2005).
16    Zhang, X., Song, J., “RBF neural networks based on dy-namic nearest neighbor-clustering algorithm and its ap-plication in prediction of MH-Ni battery capacity”, Transactions of China Electrotechnical Society, 20(11), 84—87(2005).
17    Hou, Y.B., Wang M., Wang, L.Q., System Identification and Its MATLAB Simulation, Science Press, Beijing (2004).
18    Li, M.Q., Kou, J.S., Basic Theories and Applications of Genetic Algorithm, Science Press, Beijing (2002).
19    Liu, G.P., Kadirkamanathan, V., Billings, S.A., “On-line identification of nonlinear systems using Volterra poly-nomial basis function neural networks”, Neural Netw., 11(9), 1645—1657(1998).
20    Chen, C.T., Peng, S.T., “Learning control of process sys-tems with hard input constraints”, J. Process Control, 9(2), 151—160(1999).

基于剪接系统的遗传算法RBF网络建模方法

Splicing system based genetic algorithms for developing RBF networks models

PDF (PC)

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价