Titlebook: New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy L; Jonathan Amezcua,Patricia Melin,Osca

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New Classification Method Based on Modular Neural Networks with the LVQ Algorithm and Type-2 Fuzzy L978-3-319-73773-7Series ISSN 2191-530X Series E-ISSN 2191-5318

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Introduction, objects have their own attributes, and which attributes are considered for classifying an object (or event) will depend on the problem to work with. For example, a heart disease could be classified using data obtained from a Holter device, a tumor or a cancer cell could be classified based on the data of an image.

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Proposed Classification Method,he LVQ algorithm, letting the fuzzy system to determine which of the cluster centers is the nearest to an input vector, and then moving the cluster center either towards or away from the input vector. Fuzz LVQ modular architectures were developed in order to test two datasets.

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Book 2018n (LVQ) and type-2 fuzzy logic.? This computational model consists of the hybridization of the aforementioned techniques, using a fuzzy logic system within the competitive layer of the LVQ network to determine the shortest distance between a centroid and an input vector. This new model is based on a

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Problem Statement,d for classifying faulty LEDs, in Fallah et al. (Proceedings of International MultiConference of Engineers and Computer Scientists, IMECS 2010. Hong Kong [.]) LVQ was used for iris recognition and classification using an artificial vision system. In addition in recent work LVQ has been also used for