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GA-Based RBF Neural Network for Nonlinear SISO System

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Abstract

Radial basis function (RBF) neural network is efficient to model nonlinear systems with its simpler network structure and faster learning capability. The temperature and pressure modeling of the coke furnace in an industrial coke equipment is not very easy due to disturbances, nonlinearity, and switches of coke towers. To construct the temperature and pressure models in a coke furnace, RBF neural network is utilized to improve the modeling precision. Moreover, the shortcoming of RBF neural network, such as over-fitting is overcome.

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References

  1. Broomhead, D.S., and D. Lowe. 1988. Multivariable functional interpolation and adaptive networks. Complex Systems 2 (3): 321–355.

    MathSciNet  MATH  Google Scholar 

  2. Wei, C., and J. Qiao. 2014. Passive robust fault detection using RBF neural modeling based on set membership identification. Engineering Applications of Artificial Intelligence 28 (1): 1–12.

    Google Scholar 

  3. Wilamowski, B.M., et al. 2015. A novel RBF training algorithm for short-term electric load forecasting and comparative studies. IEEE Transactions on Industrial Electronics 62 (10): 6519–6529.

    Google Scholar 

  4. Reiner, P., and B.M. Wilamowski. 2015. Efficient incremental construction of RBF networks using quasi-gradient method. 150: 349–356.

    Google Scholar 

  5. Ahmadizar, F., et al. 2015. Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm. Engineering Applications of Artificial Intelligence 39: 1–13.

    MathSciNet  Google Scholar 

  6. Wu, J., L. Jin, and M. Liu. 2015. Evolving RBF neural networks for rainfall prediction using hybrid particle swarm optimization and genetic algorithm. Neurocomputing 148 (2): 136–142.

    Google Scholar 

  7. Zhang, R., J. Tao, and F. Gao. 2014. Temperature modeling in a coke furnace with an improved RNA-GA based RBF network. Industrial Engineering Chemistry Research 53 (8): 3236–3245.

    Google Scholar 

  8. Wang, Y., and P. Yao. 2003. Simulation and optimization for thermally coupled distillation using artificial neural network and genetic algorithm. Chinese Journal of Chemical Engineering 11 (3): 307–311.

    Google Scholar 

  9. Yang, T., H.C. Lin, and M.L. Chen. 2006. Metamodeling approach in solving the machine parameters optimization problem using neural network and genetic algorithms: A case study. Robotics Computer Integrated Manufacturing 22 (4): 322–331.

    Google Scholar 

  10. Blanco, A., M. Delgado, and M.C. Pegalajar. 2001. A real-coded genetic algorithm for training recurrent neural networks. Journal of the International Neural Network Society 14 (1): 93–105.

    Google Scholar 

  11. Delgado, M., and M.C. Pegalajar. 2005. A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference. Pattern Recognition 38 (9): 1444–1456.

    MATH  Google Scholar 

  12. Yang, L. and J. Yen. 2010. An Adaptive Simplex Genetic algorithm. in Genetic & Evolutionary Computation Conference.

    Google Scholar 

  13. Esposito, A., et al. 2000. Approximation of continuous and discontinuous mappings by a growing neural RBF-based algorithm. Neural Networks the Official Journal of the International Neural Network Society 13 (6): 651–665.

    Google Scholar 

  14. Sarimveis, H., et al. 2004. A new algorithm for developing dynamic radial basis function neural network models based on genetic algorithms. Computers Chemical Engineering Journal 28 (1–2): 209–217.

    Google Scholar 

  15. Guang-Bin, H., P. Saratchandran, and S. Narasimhan. 2005. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Transactions on Neural Networks 16 (1): 57–67.

    Google Scholar 

  16. Du, D., L. Kang, and M. Fei. 2010. A fast multi-output RBF neural network construction method. Neurocomputing 73 (10): 2196–2202.

    Google Scholar 

  17. Han, H.G., Q.L. Chen, and J.F. Qiao. 2011. An efficient self-organizing RBF neural network for water quality prediction. Neural Networks the Official Journal of the International Neural Network Society 24 (7): 717–725.

    MATH  Google Scholar 

  18. Chen, X., and N. Wang. 2009. A DNA based genetic algorithm for parameter estimation in the hydrogenation reaction. Chemical Engineering Journal 150 (2): 527–535.

    Google Scholar 

  19. Tao, J., and N. Wang. 2007. DNA computing based RNA genetic algorithm with applications in parameter estimation of chemical engineering processes. Computers Chemical Engineering Journal 31 (12): 1602–1618.

    Google Scholar 

  20. Dayal, B.S., and J.F. Macgregor. 1997. Improved PLS algorithms. Journal of Chemometrics 11 (1): 73–85.

    Google Scholar 

  21. Zhang, R., J. Tao, and F. Gao. 2016. A new approach of takagi-sugeno fuzzy modeling using an improved genetic algorithm optimization for oxygen content in a coke furnace. Industrial Engineering Chemistry Research 55 (22): 6465–6474.

    Google Scholar 

  22. Zhang, R., et al. 2016. New minmax linear quadratic fault-tolerant tracking control for batch processes. IEEE Transactions on Automatic Control 61 (10): 3045–3051.

    MathSciNet  MATH  Google Scholar 

  23. Brusco, M.J. 2014. A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis. Computational Statistics & Data Analysis 77: 38–53.

    MathSciNet  MATH  Google Scholar 

  24. Li, J., C. Duan, and Z. Fei. 2016. A Novel Variable Selection Approach for Redundant Information Elimination Purpose of Process Control. IEEE Transactions on Industrial Electronics 63 (3): 1737–1744.

    Google Scholar 

  25. Zhang, R. and J. Tao,. 2017. A nonlinear fuzzy neural network modeling approach using an improved genetic algorithm. IEEE Transactions on Industrial Electronics 65(7): 5882–5892.

    Google Scholar 

  26. Andersen, C.M., and R. Bro. 2010. Variable selection in regression—a tutorial. Journal of Chemometrics 24 (11–12): 728–737.

    Google Scholar 

  27. Sun, K., et al. 2016. Design and application of a variable selection method for multilayer perceptron neural network with LASSO. IEEE Transactions on Neural Networks and Learning Systems 28(6): 1386–1396.

    Google Scholar 

  28. Enrique, R. and S. Josep María, Romero. 2008. Performing feature selection with multilayer perceptrons. IEEE Transactions on Neural Networks 19(3): 431–441.

    Google Scholar 

  29. Souza, F.A.A., et al. 2013. A multilayer-perceptron based method for variable selection in soft sensor design. Journal of Process Control 23 (10): 1371–1378.

    Google Scholar 

  30. Estévez, P.A., et al. 2009. Normalized mutual information feature selection. IEEE Transactions on Neural Networks 20 (2): 189–201.

    MathSciNet  Google Scholar 

  31. Zhang, R., et al. 2016. Decoupled ARX and RBF neural network modeling using PCA and GA optimization for nonlinear distributed parameter systems. IEEE Transactions on Neural Networks Learning Systems 29 (2): 457–469.

    MathSciNet  Google Scholar 

  32. Pacheco, J., S. Casado, and S. Porras. 2013. Exact methods for variable selection in principal component analysis: Guide functions and pre-selection. Computational Statistics Data Analysis 57 (1): 95–111.

    MathSciNet  MATH  Google Scholar 

  33. Puggini, L., and S. Mcloone. 2017. Forward selection component analysis: Algorithms and applications. IEEE Transactions on Pattern Analysis Machine Intelligence 39 (12): 2395–2408.

    Google Scholar 

  34. Chen, J. 2004. Computational aspects of algorithms for variable selection in the context of principal components. Computational Statistics Data Analysis 47 (2): 225–236.

    MathSciNet  Google Scholar 

  35. Cadima, J.F.C.L., and I.T. Jolliffe. 2001. Variable selection and the interpretation of principal subspaces. Journal of Agricultural Biological Environmental Statistics 6 (1): 62–79.

    MathSciNet  Google Scholar 

  36. Brusco, M.J. 2014. A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis. Computational Statistics & Data Analysis 77 (9): 38–53.

    MathSciNet  MATH  Google Scholar 

  37. Li, C.J., et al. 2018. Near-optimal stochastic approximation for online principal component estimation. Mathematical Programming 167 (1): 75–97.

    MathSciNet  MATH  Google Scholar 

  38. Wei-Shi, Z., L. Jian-Huang, and P.C. Yuen. 2005. GA-fisher: A new LDA-based face recognition algorithm with selection of principal components. IEEE Transactions on Systems, Man and Cybernetics Part B 35 (5): 1065–1078.

    Google Scholar 

  39. Brusco, Michael J. 2017. A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis. Computational Statistics & Data Analysis 77: 38–53.

    Google Scholar 

  40. Nchare, M., B. Shen, and S.G. Anagho. 2012. Co-processing vacuum residue with waste plastics in a delayed coking process: Kinetics and modeling. China Petroleum Processing Petrochemical Technology 14 (3): 44–49.

    Google Scholar 

  41. Bello, O.O., et al. 2006. Effects of operating conditions on compositional characteristics and reaction kinetics of liquid derived by delayed coking of Nigerian petroleum residue. Brazilian Journal of Chemical Engineering 23 (3): 331–339.

    MathSciNet  Google Scholar 

  42. Zhang, R., and S. Wang. 2008. Support vector machine based predictive functional control design for output temperature of coking furnace. Journal of Process Control 18 (5): 439–448.

    Google Scholar 

  43. Zhang, R., and J. Tao. 2017. Data-driven modeling using improved multi-objective optimization based neural network for coke furnace system. IEEE Transactions on Industrial Electronics 64 (4): 3147–3155.

    Google Scholar 

  44. Zhang, R., Q. Lv, J. Tao, et al. 2018. Data driven modeling using an optimal principle component analysis based neural network and its application to a nonlinear coke furnace. Industrial and Engineering Chemistry Research 57 (18): 6344–6352.

    Google Scholar 

  45. Tao, J., X. Chen, and Z. Yong. 2012. Constraint multi-objective automated synthesis for CMOS operational amplifier. Neurocomputing 98 (18): 108–113.

    Google Scholar 

  46. Li, Y., C. Fang, and Q. Ouyang. 2004. Genetic algorithm in DNA computing: A solution to the maximal clique problem. Chinese Science Bulletin 49 (9): 967–971.

    MathSciNet  MATH  Google Scholar 

  47. Martin, N. and H. Maes. 1979 Multivariate analysis. London: Academic Press.

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, NingboTech University, Ningbo, Zhejiang, China

    Jili Tao & Yong Zhu

  2. The Belt and Road Information Research Institute, Hangzhou Dianzi University, Hangzhou, Zhejiang, China

    Ridong Zhang

Authors
  1. Jili Tao
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  2. Ridong Zhang
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  3. Yong Zhu
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Corresponding author

Correspondence to Jili Tao .

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Tao, J., Zhang, R., Zhu, Y. (2020). GA-Based RBF Neural Network for Nonlinear SISO System. In: DNA Computing Based Genetic Algorithm. Springer, Singapore. https://doi.org/10.1007/978-981-15-5403-2_6

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