A fuzzy ensemble of parallel polynomial neural networks with information granules formed by fuzzy clustering |
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| Authors: | Seok-Beom Roh Sung-Kwun Oh Witold Pedrycz |
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| Affiliation: | 1. Dept. of Electrical Electronic and Information Engineering, Wonkwang Univ., 344-2, Shinyong-Dong, Iksan, Chon-Buk 570-749, South Korea;2. Dept. of Electrical Engineering, The University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do 445-743, South Korea;3. Dept. of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada T6G 2G6;4. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;1. Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4, AB, Canada;2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Smt. Chandaben Mohanbhai Patel Institute of Computer Applications, CHARUSAT Campus, Changa, Anand 388421, Gujarat, India;2. Department of Computer Science & Engineering, Thapar Institute of Engineering and Technology, Patiala 147004, Punjab, India;1. Research Center of Information and Control, Dalian University of Technology, Dalian 116024, China;2. School of Mathematics and System Science, Shenyang Normal University, Shenyang 110034, China;3. Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, AB, Canada;1. School of Information Sciences, Nanjing Audit University, Nanjing 211815, PR China;2. Information Systems Auditing Experimental Center, Nanjing Audit University, Nanjing 211815, PR China;3. School of Engineering and Management, Nanjing University, Nanjing 210093, PR China |
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| Abstract: | In this paper, we introduce a new category of fuzzy models called a fuzzy ensemble of parallel polynomial neural network (FEP2N2), which consist of a series of polynomial neural networks weighted by activation levels of information granules formed with the use of fuzzy clustering. The two underlying design mechanisms of the proposed networks rely on information granules resulting from the use of fuzzy C-means clustering (FCM) and take advantage of polynomial neural networks (PNNs).The resulting model comes in the form of parallel polynomial neural networks. In the design procedure, in order to estimate the optimal values of the coefficients of polynomial neural networks we use a weighted least square estimation algorithm. We incorporate various types of structures as the consequent part of the fuzzy model when using the learning algorithm. Among the diverse structures being available, we consider polynomial neural networks, which exhibit highly nonlinear characteristics when being viewed as local learning models.We use FCM to form information granules and to overcome the high dimensionality problem. We adopt PNNs to find the optimal local models, which can describe the relationship between the input variables and output variable within some local region of the input space.We show that the generalization capabilities as well as the approximation abilities of the proposed model are improved as a result of using polynomial neural networks. The performance of the network is quantified through experimentation in which we use a number of benchmarks already exploited within the realm of fuzzy or neurofuzzy modeling. |
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