NDLI: Fuzzy gated neuronal architecture for pattern recognition

Content Provider	IEEE Xplore Digital Library
Author	Chandrasekaran, V. Zhi-Qiang Liu Palaniswami, M.
Copyright Year	1995
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Melbourne Univ., Carlton, Vic., Australia (Chandrasekaran, V.; Zhi-Qiang Liu; Palaniswami, M.)
Abstract	In this paper, a novel fuzzy gated neuronal architecture capable of utilizing all possible combinations of the decision planes between the points represented by weights in n-dimensional feature space is proposed. This is achieved by selecting a set of nodes based on an eligibility criteria and then letting these selected nodes to compete. The time sequence of winning nodes generated by a time-varying eligibility criterion provides a time-indexed expert opinions in respect of the class membership grades. These opinions when combined properly enhance the class label prediction accuracies to a great extent. The architecture is built on a fuzzy gated neuron model and a set of gate control functions. In addition, it is shown that the training of weights to represent the cluster centroids is not necessary resulting in a quick network set up time. The classification performance of the proposed network on a difficult 12-class synthetic 3-D object recognition problem indicates excellent results.
Starting Page	1622
Ending Page	1627
File Size	470418
Page Count	6
File Format	PDF
ISBN	0780327683
DOI	10.1109/ICNN.1995.488861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1995-11-27
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern recognition Subspace constraints Computer architecture Computer science Australia Accuracy Fuzzy sets Neurons Object recognition Signal processing
Content Type	Text
Resource Type	Article

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