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| A Novel Hybrid Voter Using Genetic Algorithm and Performance History
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Full
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 PDF(732.6KB) |
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Source |
International Journal of Artificial Intelligence and Expert Systems (IJAE) |
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Table of Contents |
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Complete Issue PDF(2.42MB) |
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Volume: 2 Issue: 3 |
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Pages: 96-149 |
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Publication
Date: July / August 2011 |
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ISSN
(Online): 2180-124X |
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Pages |
117 - 125 |
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Author(s) |
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Published
Date |
05-08-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: TMR, Soft threshold, Genetic Algorithm, Weighted Average Voting |
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| This Manuscript is indexed in the following databases/websites:- |
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| 1. Directory of Open Access Journals (DOAJ) |
| 2. Google Scholar |
| 3. Scribd |
| 4. Docstoc |
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| Triple Modular Redundancy (TMR) is generally used to increase the reliability of real time systems where three similar modules are used in parallel and the final output is arrived at using voting methods. Numerous majority voting techniques have been proposed in literature however their performances are compromised for some typical set of module output value. Here we propose a new voting scheme for analog systems retaining the advantages of previous reported schemes and reduce the disadvantages associated with them. The scheme utilizes a genetic algorithm and previous performances history of the modules to calculate the final output. The scheme has been simulated using MATLAB and the performance of the voter has been compared with that of fuzzy voter proposed by Shabgahi et al [4]. The performance of the voter proposed here is better than the existing voters. |
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| AKHILESH PATHAK : Colleagues
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| Anand Mohan : Colleagues
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