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.

Received: 22 August 2014/Accepted: 23 June 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

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*Corresponding author; email: cjlin@ncut.edu.tw