Modular general fuzzy hypersphere neural network

Patil, Pradeep M.; Kulkarni, Suprit; Patil, Ankit; Doye, Dharmpal D.; Kulkarni, Uday

doi:10.1109/ictai.2005.86

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…The evaluation was done on the Fisher Iris dataset and the observed accuracy was 52.51%. Lastly, P. M. Patil et al [6] presented a Modular GFHSNN (MGFHSNN), an extension to the GFHSNN which used the combination of the pair of unsupervised learning and supervised learning approaches into one method for classification, clustering and hybrid classification, and clustering. It provided a high degree of parallelism and was evaluated on the fisher iris dataset and obtained an accuracy of 72.65%.…”

Section: Literature Reviewmentioning

confidence: 99%

Pattern Classification Using Supervised Hypersphere Neural Network0

Mane¹,

Moorthy²,

Kumbharkar³

et al. 2022

IJETAE

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Pattern classification is of significant demand in the field of machine learning. Its applications range from a simple problem of speech recognition to a complex and important problem of medical diagnosis. Fuzzy based algorithms have been one of the most important methods which have contributed in solving the pattern classification problem. A customized Fuzzy based Supervised Hypersphere Neural Network (SHNN) is presented for the use of pattern classification. Here, a modernized pattern classification method has been presented by considering the fuzzy hypersphere neural network concept at the back end and using a modified version of the membership function aiming to solve the pattern classification problems and boost the performance of the algorithm. The proposed SHNN model creates supervised hypersphere using measurements obtained from intra-class distance techniques along with individual class pattern choice, over the fuzzy membership function. The previous modified fuzzy approaches presented an inherent drawback of ambiguous assignment of classes, losing the fuzzy nature during the assigning of classes in the testing phase and over fitting of the model during the training phase. The proposed approach solves this problem by adding a non linearity to the output of the membership function to maintain its fuzzy nature. Additionally, a new weighted Euclidean distance equation has been designed to enhance the performance of the algorithm. The performance of the proposed model of SHNN has been examined on four standard datasets namely - Pima, liver, glass and monks-3. The results obtained were superior to the previous proposed approaches. Thus, presenting a new state result of the art on the datasets.

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Section: Literature Reviewmentioning

confidence: 99%

Pattern Classification Using Supervised Hypersphere Neural Network0

Mane¹,

Moorthy²,

Kumbharkar³

et al. 2022

IJETAE

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show abstract

“…Sintered pellets rejected due to nonconformation of chemical specification ('O', 'N', 'C' and M 2 C 3 ) are crushed into powder followed by controlled oxidation to obtain oxide powder with (O/M) ratio of 2.17-2.20 [15]. After adjusting the Pu/U ratio, oxide powders are co-milled with graphite powder followed by carbothermic reduction and sintering.…”

Section: New Process Developments In Fuel Productionmentioning

confidence: 99%

Experience on mixed carbide fuels with high ‘Pu’ content for Indian fast breeder reactor – An overview

Sengupta

Basak

Kumar

et al. 2009

Journal of Nuclear Materials

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“…Moreover, Modular general fuzzy hypersphere neural network in [27] the contribution in [13] is extended further proposing modular approach which leads in decrease of computational complexity due to parallelism. Tables I, II and III gives a summary of FNNs discussed in the sections II, III and IV, respectively.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

Review on Classification and Clustering using Fuzzy Neural Networks

Kulkarni¹,

Honwadkar²

2016

IJCA

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In data mining two important tasks involved are classification and clustering. In general, in classification the classifier assigns a class label from a set of predefined classes to a new input object. Whereas, given a set of objects, clustering creates different groups of these objects using some similarity measure. In the context of machine learning, classification is supervised learning and clustering is unsupervised learning. There are different approaches used for classification and clustering. In recent past many fuzzy neural networks have been proposed which can be employed for classification and clustering. Unlike other techniques, the fuzzy neural networks are quickly trainable, suitable for online training, provides soft decision, and capable of constructing nonlinear decision boundaries. All these benefits make them suitable for difficult real world problems involving classification and clustering. This paper provides review on recent fuzzy neural learning algorithms and mainly focusing on pattern/object classification and clustering.

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Modular general fuzzy hypersphere neural network

Cited by 6 publications

References 11 publications

Pattern Classification Using Supervised Hypersphere Neural Network0

Pattern Classification Using Supervised Hypersphere Neural Network0

Experience on mixed carbide fuels with high ‘Pu’ content for Indian fast breeder reactor – An overview

Review on Classification and Clustering using Fuzzy Neural Networks

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