An improved swarm optimized functional link artificial neural network (ISO-FLANN) for classification |
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| Authors: | Satchidananda Dehuri Rahul Roy Sung-Bae Cho Ashish Ghosh |
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| Affiliation: | 1. Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore 756 019, India;2. Machine Intelligence Unit, Indian Statistical Unit, 203 B. T. Road, Kolkata 700 108, India;3. Soft Computing Laboratory, Department of Computer Science, Yonsei University, 262 Seongsanno, Seodaemun-gu, Seoul 120-749, South Korea;4. Center for Soft Computing Research, Indian Statistical Institute, 203 B. T. Road, Kolkata 700 108, India;1. Process Modeling and Instrumentation Cell, CSIR – Institute of Minerals and Materials Technology, Bhubaneswar 751013, India;2. Department of Electrical Engineering, National Institute of Technology, Rourkela, India;1. School of Automation, Beijing University of Posts and Telecommunications, Beijing 100876, China;2. School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia;1. V?B-Technical University of Ostrava, Faculty of Electrical Engineering and Computer Science, Department of Computer Science, 17. listopadu 15/2172 Ostrava, Czech Republic;2. Department of Electrical & Computer Engineering, University of Alberta, Edmonton T6R 2V4 AB, Canada;3. Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, 21589, Saudi Arabia;4. Systems Research Institute, Polish Academy of Sciences Warsaw, Poland;1. Department of Chemical Engineering, Faculty of Engineering, Srinakharinwirot University, Nakhon Nayok 26120, Thailand;2. Computational Process Engineering Research Unit, Department of Chemical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand |
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| Abstract: | Multilayer perceptron (MLP) (trained with back propagation learning algorithm) takes large computational time. The complexity of the network increases as the number of layers and number of nodes in layers increases. Further, it is also very difficult to decide the number of nodes in a layer and the number of layers in the network required for solving a problem a priori. In this paper an improved particle swarm optimization (IPSO) is used to train the functional link artificial neural network (FLANN) for classification and we name it ISO-FLANN. In contrast to MLP, FLANN has less architectural complexity, easier to train, and more insight may be gained in the classification problem. Further, we rely on global classification capabilities of IPSO to explore the entire weight space, which is plagued by a host of local optima. Using the functionally expanded features; FLANN overcomes the non-linear nature of problems. We believe that the combined efforts of FLANN and IPSO (IPSO + FLANN = ISO ? FLANN) by harnessing their best attributes can give rise to a robust classifier. An extensive simulation study is presented to show the effectiveness of proposed classifier. Results are compared with MLP, support vector machine(SVM) with radial basis function (RBF) kernel, FLANN with gradiend descent learning and fuzzy swarm net (FSN). |
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