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MODIFIED FUZZY HYPER-LINE SEGMENT CLUSTERING NEURAL NETWORK (MFHLSCNN) FOR PATTERN RECOGNITION AND ITS PARALLEL IMPLEMENTATION ON GPU
Priyadarshan Dhabe #1, Akshay Prakash Mahajan *2

Published in: Journal for Advance Research in Applied Sciences
Volume- 4, Issue-1, pp.490-495, Jun 2017
DPI :-> 16.10089.JARAS.2017.V4I1.490495.1760



Abstract
The Modified Fuzzy Hyper-Line Segment Clustering Neural Network (MFHLSCNN) is the modified version of Fuzzy Hyper-Line Segment Clustering Neural Network (FHLSCNN) [1]. This hybrid system combing fuzzy logic and neural networks is used for pattern recognition. MFHLSCNN learn patterns in terms of n – dimensional Hyper-Line Segments (HLSs), which are fuzzy sets. The fuzzy HLSs created during the training of MFHLSCNN and are defined by two endpoints. After HLSs creation, we are clustering them and removing clustered HLSs based on the membership criteria iteratively. For a large dataset, MFHLSCNN creates large no of HLSs and causes an increase in time in training and testing phase. In this work, we proposed a GPU (Graphics Processing Unit) parallel implementation of MFHLSCNN using CUDA [2] and achieved 4.64 times speedup, for online retail data set [3]. For parallel implementation, we used NVIDIA’s single Tesla K20 GPU and CUDA (Compute Unified Device Architecture) computing platform.

Key-Words / Index Term
CUDA, Fuzzy Neural Network, Parallel computing on GPU, Pattern Recognition, Market segmentation.

How to cite this article
Priyadarshan Dhabe #1, Akshay Prakash Mahajan *2 , “MODIFIED FUZZY HYPER-LINE SEGMENT CLUSTERING NEURAL NETWORK (MFHLSCNN) FOR PATTERN RECOGNITION AND ITS PARALLEL IMPLEMENTATION ON GPU ”, Journal for Advance Research in Applied Sciences, 4, Issue-1, pp.490-495, Jun 2017. DPI:16.10089.JARAS.V4.I1.1760