A Maximizing-Discriminability-Based Architecture for Fuzzy-Neural-Network Hardware
Authors: 
Gin-Der Wu, Zhen-Wei ZhuAuthors Info & Claims
Gin-Der Wu, Zhen-Wei ZhuAuthors Info & ClaimsPages 153 - 156
Abstract
A maximizing-discriminability-based architecture for fuzzy-neural-network (FNN) hardware is proposed in this paper. The major contribution of this proposed FNN hardware is to increase the discriminative capability among different classes in classification problems by combining linear discriminant analysis (LDA) and Gaussian mixture model (GMM). In LDA, the weights are updated by seeking directions that are efficient for discrimination. In GMM, the parameter learning adopts the gradient descent method to reduce the cost function. Furthermore, this FNN can be reconfigured by the instruction of the external processer.
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Published In
February 2017
545 pages
ISBN:9781450348171
DOI:10.1145/3055635
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- Southwest Jiaotong University
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Association for Computing Machinery
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Published: 24 February 2017
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ICMLC 2017
ICMLC 2017: 2017 the 9th International Conference on Machine Learning and Computing
February 24 - 26, 2017
Singapore, Singapore
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