some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted ave
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some applications are critical and must designed Fault Tolerant System. Usually Voting Algorithm is one of the principle elements of a Fault Tolerant System. Two kinds of voting algorithm are used in most applications, they are majority voting algorithm and weighted average algorithm these algorithms have some problems. Majority confronts with the problem of threshold limits and voter of weighted average are not able to produce safe outputs when obtaining a correct output is impossible and also both of them are not able to perform appropriately in small error limit. In the present paper, delivering a voter for safety system, Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed. The above mentioned model is trained through Hybrid learning algorithm that is effective and using basic Fuzzy inference system, subtractive clustering and fuzzy C-means method. Results show that delivered voter produced more safety outputs especially for small error amplitude.Keywords: ANFIS, Adaptive Neuro-Fuzzy Inference System, Voting Algorithm, Fault Tolerant Systems, Safety-Critical Systems.
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