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SVM-BASED ROBUST TEMPLATE DESIGN FOR CELLULAR NEURAL NETWORKS IMPLEMENTING AN ARBITRARY BOOLEAN FUNCTION

In this thesis, the geometric margin is used for the first time as the robustness indicator of an uncoupled cellular neural network implementing a given Boolean function. First, robust template design for uncoupled cellular neural networks implementing linearly separable Boolean functions by support vector machines is proposed. A fast sequential minimal optimization algorithm is presented to find maximal margin classifiers, which in turn determine the robust templates. Some general properties of robust templates are investigated. An improved CFC algorithm implementing an arbitrarily given Boolean function is proposed. Two illustrative examples are provided to demonstrate the validity of the proposed method.

Identiferoai:union.ndltd.org:NSYSU/oai:NSYSU:etd-0627105-134953
Date27 June 2005
CreatorsTeng, Wei-chih
ContributorsJer-guang Hsieh, Jyh-horng Jeng, Chang-hua Lien
PublisherNSYSU
Source SetsNSYSU Electronic Thesis and Dissertation Archive
LanguageEnglish
Detected LanguageEnglish
Typetext
Formatapplication/pdf
Sourcehttp://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0627105-134953
Rightsunrestricted, Copyright information available at source archive

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