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The novel sequence bound global particle swarm optimizer can solve the problem of premature convergence when learning the fuzzy network plagued with many local optimal solutions. Unlike multi-layer perceptron with many hidden layers it has only single hidden layer. The output layer of this network contains one neuron. This network advocates a simple and understandable architecture for classification. The experimental studies show that the classification accuracy of the proposed algorithm is promising and superior to other alternatives such as multi-layer perceptron and radial basis function network.<\/p>","DOI":"10.4018\/ijfsa.2012010104","type":"journal-article","created":{"date-parts":[[2012,4,5]],"date-time":"2012-04-05T09:13:24Z","timestamp":1333617204000},"page":"54-70","source":"Crossref","is-referenced-by-count":6,"title":["Learning Fuzzy Network Using Sequence Bound Global Particle Swarm Optimizer"],"prefix":"10.4018","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1435-4531","authenticated-orcid":true,"given":"Satchidananda","family":"Dehuri","sequence":"first","affiliation":[{"name":"Fakir Mohan University, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sung-Bae","family":"Cho","sequence":"additional","affiliation":[{"name":"Yonsei University, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijfsa.2012010104-0","doi-asserted-by":"publisher","DOI":"10.1109\/91.618273"},{"key":"ijfsa.2012010104-1","doi-asserted-by":"crossref","unstructured":"Agrawal, R., Imielinski, T., & Swami, A. 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