Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network |
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| Affiliation: | 1. School of Mechanical and Building Science, VIT University, Chennai 600127, India;2. Department of Mechanical Engineering, Amrita School of Engineering Amrita Vishwa Vidyapeetham, Coiambatore, India;1. School of Management, Fuzhou University, Fuzhou, Fujian 350108, China;2. College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, China;1. The University of Tokyo, 7-3-1 Hongo Bunkyo-ku, Tokyo 113-8656, Japan;2. Lancers Inc., 3-10-13 Shibuya Shibuya-ku, Tokyo 150-0002, Japan;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. Department of Applied Systems, Hanyang University, Seoul 133-791, Republic of Korea;2. Department of Business Administration, Daegu University, Kyong San 712-714, Republic of Korea |
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| Abstract: | Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction–repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if–then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models. |
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| Keywords: | Fuzzy neural networks Electromagnetism-like mechanism Back propagation algorithm Weights elimination |
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