Sains Malaysiana 44(12)(2015): 1721–1728

An Interactively Recurrent Functional Neural Fuzzy Network with Fuzzy Differential Evolution and Its Applications

(Rangkaian Neuron Kabur Berfungsi Interaktif Berulang dengan Evolusi Pengkamiran Kabur dan Penggunaannya)

CHENG-JIAN LIN*¹, CHIH-FENG WU², HSUEH-YI LIN¹ & CHENG-YI YU¹

¹Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung City 411, Taiwan, R.O.C.

²Department of Digital Content Application and Management, Wenzao Ursuline University of Languages, Kaohsiung City 807, Taiwan, R.O.C.

Diserahkan: 22 Ogos 2014/Diterima: 23 Jun 2015

ABSTRACT

In this paper, an interactively recurrent functional neural fuzzy network (IRFNFN) with fuzzy differential evolution (FDE) learning method was proposed for solving the control and the prediction problems. The traditional differential evolution (DE) method easily gets trapped in a local optimum during the learning process, but the proposed fuzzy differential evolution algorithm can overcome this shortcoming. Through the information sharing of nodes in the interactive layer, the proposed IRFNFN can effectively reduce the number of required rule nodes and improve the overall performance of the network. Finally, the IRFNFN model and associated FDE learning algorithm were applied to the control system of the water bath temperature and the forecast of the sunspot number. The experimental results demonstrate the effectiveness of the proposed method.

Keywords: Control; differential evolution; neural fuzzy network; prediction; recurrent network

ABSTRAK

Dalam kajian ini, rangkaian neuron kabur berfungsi interaktif berulang (IRFNFN) dengan kaedah pembelajaran evolusi pengkamiran kabur (FDE) dicadangkan untuk menyelesaikan masalah kawalan dan ramalan. Kaedah tradisi evolusi pengkamiran (DE) akan terperangkap dengan mudah di dalam optimum tempatan semasa proses pembelajaran, tetapi evolusi pengkamiran kabur algoritma yang dicadangkan boleh mengatasi kelemahan ini. Melalui perkongsian maklumat nod dalam lapisan interaktif, IRFNFN yang dicadangkan boleh mengurangkan bilangan nod peraturan yang diperlukan dengan berkesan dan meningkatkan prestasi keseluruhan rangkaian. Akhir sekali, gabungan model IRFNFN dan pembelajaran algoritma FDE digunakan untuk sistem kawalan suhu rendaman air dan ramalan nombor tompok matahari. Keputusan eksperimen menunjukkan keberkesanan kaedah yang dicadangkan.

Kata kunci: Evolusi pengkamiran; kawalan; ramalan; rangkaian berulang; rangkaian neuron kabur

RUJUKAN

Ali, M.M., Khompatraporn, C. & Zabinsky, Z.B. 2005. A numerical evaluation of several stochastic algorithms on selected continuous global optimization test problems. Journal of Global Optimization 31(4): 635-672.

Chen, C.S. 2010. TSK-type self-organizing recurrent-neural-fuzzy control of linear microstepping motor drives. IEEE Transactions on Power Electronics 25(9): 2253-2265.

Chen, C.H., Su, M.T., Lin, C.J. & Lin, C.T. 2014. A hybrid of bacterial foraging optimization and particle swarm optimization for evolutionary neural fuzzy classifier design. International Journal of Fuzzy Systems 16(3): 422-433.

Chen, C.H., Lin, C.J. & Lin, C.T. 2009. Nonlinear system control using adaptive neural fuzzy networks based on a modified differential evolution. IEEE Trans. On Systems, Man, and cybernetics-Part C: Applications and Reviews 39(4): 459- 473.

Chen, Y.C. & Teng, C.C. 1995. A model reference control structure using a fuzzy neural network. Fuzzy Sets and Systems 73: 291-312.

Gong, W., Cai, Z.C., Ling, X. & Li, H. 2011. Enhanced differential evolution with adaptive strategies for numerical optimization. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 41(2): 397-413.

Huang, V.L., Qin, A.K. & Suganthan, P.N. 2006. Self-adaptive differential evolution algorithm for constrained real-parameter optimization. IEEE Congress on Evolutionary Computation pp. 215-222.

Juang, C.F. 2002. A TSK-type recurrent fuzzy network for dynamic systems processing by neural network and genetic algorithms. IEEE Trans. on Fuzzy Systems 10(2): 155-170.

Juang, C.F. & Hsieh, C.D. 2010. A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling. IEEE Transactions on Fuzzy Systems 18(2): 261-273.

Lee, C.H. & Teng, C.C. 2000. Identification and control of dynamic systems using recurrent fuzzy neural networks. IEEE Trans. on Fuzzy Systems 8(4): 349-366.

Li, J., Cheng, J.H., Shi, J.Y. & Huang, F. 2012. Brief introduction of back propagation (BP) neural network algorithm and its improvement. In Advances in Computer Science and Information Engineering. Berlin, Heidelberg: Springer. pp. 553-558.

Lin, C.J. 2004. A GA-based neural fuzzy system for temperature control. Fuzzy Sets and Systems 143(2): 311-333.

Lin, C.J., Lin, Y.M. & Lee, C.Y. 2010. Nonlinear system control using a recurrent neural fuzzy network based on reinforcement particle swarm optimization. The 3rd International Symposium on Computational Intelligence and Design (ISCID2010). pp. 196-200.

Lin, C.J., Chen, C.H. & Lin, C.T. 2009. A hybrid of cooperative particle swarm optimization and cultural algorithm for neural fuzzy network and its prediction applications. IEEE Trans. on Systems, Man, and Cybernetics--Part C: Applications and Reviews 38(1): 55-68.

Montgomery, J. 2010. Crossover and the different faces of differential evolution searches. Proc. of the IEEE Congress on Evolutionary Computation. pp. 1804-1811.

Montgomery, J. & Chen, S. 2010. An analysis of the operation of differential evolution at high and low crossover rates. Proc. of the IEEE Congress on Evolutionary Computation. pp. 881-888.

Natsuki, H. & Hitoshi, I. 2003. Particle swarm optimization with Gaussian mutation. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. pp. 72-79.

Neri, F. & Tirronen, V. 2010. Recent advances in differential evolution: A survey and experimental analysis. Artificial Intelligence Review 33(1-2): 61-106.

Ronkkonen, J., Kukkonen, S. & Price, K.V. 2005. Real-parameter optimization with differential evolution. Proceedings of IEEE Congress on Evolutionary Computation. pp. 506-513.

Saruhan, H. 2014. Differential evolution and simulated annealing algorithms formechanical systems design. Engineering Science and Technology, an International Journal 17(3): 131-136.

Shang, Y.W. & Qin, Y.H. 2006. A note on the extended rosenbrock function. Evolutionary Computation 14(1): 119-126.

Simon, D. 2013. Evolutionary Optimization Algorithms. New York: Wiley.

Storm, R. & Price, K. 1997. Differential evolution - A simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4): 341-359.

Yang, Z., Tang, K. & Yao, X. 2010. Scalability of generalized adaptive differential evolution for large-scale continuous optimization. Soft Computing 15(11): 2141-2155.

Wang, J.Z., Wang, J.J., Zhang, Z.G. & Guo, S.P. 2011. Forecasting stock indices with back propagation neural network. Expert Systems with Applications 38(11): 14346-14355.

*Pengarang untuk surat-menyurat; email: cjlin@ncut.edu.tw