Sandeep Paul

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A new subsethood-product fuzzy neural inference system (SuPFuNIS) is presented in this paper. It has the flexibility to handle both numeric and linguistic inputs simultaneously. Numeric inputs are fuzzified by input nodes which act as tunable feature fuzzifiers. Rule based knowledge is easily translated directly into a network architecture. Connections in(More)
Over the years conventional neural networks has shown state-of-art performance on many problems. However, their performance on recognition system is still not widely accepted in the machine learning community because these networks are unable to handle selectivity-invariance dilemma and also suffer from the problem of vanishing gradients. Some of these(More)
—This paper presents an approach to time series prediction based on Asymmetric Subsethood-Product Fuzzy Neu-ral Inference System (ASuPFuNIS). The standard time series techniques have standard averaging where a fixed weight is added to the past values. In this paper we present a novel neuro-fuzzy inference system based on asymmetric subsethood with(More)
Nervous system is one of the most complex networks known to mankind. An attempt in understanding and analyzing the functionalities of the nervous system would provide solutions to various problem pertaining in fields like medicine, robotics, ergonomics, bio-mechanics, medical research etc. Basic activities like rapid eye movements (Saccades), hand movements(More)
A novel evolutionary TSK-subsethood fuzzy-neural network model along with its parallel implementation on a LAM/MPI cluster is presented in this paper. The proposed four-layered network is inspired by the subsethood class of models, which have ability to seamlessly compose numeric and linguistic data simultaneously. The proposed model embeds TSK rules into(More)