International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on In

DOI: 10.1109/cimca.2005.1631367

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Cost-Performance Co-Analysis in VLSI Implementation of Existing and New Defuzzification Methods

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Hamid Reza Mahdiani

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Cited by 12 publications

(1 citation statement)

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“…We can find a good research on this at [19], where new methods are introduced. Another form of fast defuzzifiers is presented in [20].…”

Section: Defuzzificationmentioning

confidence: 99%

Discrete Fuzzy Inference Engine Algorithm for Digital Implementations of Approximate Reasoning

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et al. 2008

2008 Third International Conference on Convergence and Hybrid Information Technology

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Fuzzy logic has become a very good choice to represent uncertain models of complex systems that cannot be easily represented in terms of conventional mathematics. Specifically fuzzy hardware has turned to be the choice to reach high speed inference rates. There are two forms to represent membership values universe: first one is when floating point numbers are used, second form is when integer universe is used. In the first case result of operations belongs to [0,1], in the second case results belong to interval of integers [0, m], where m is an integer that can be represented by a different number of bits according to application resolution demands. This case is fully compatible with digital computers because floating point operations consume much more time and resources than integer operations. This paper presents an algorithm to implement fuzzy logic inference engine for discrete implementations and new method of defuzzification procedure, with the objective of reducing the number of instructions to be executed, having as consequence fewer processing time and less resources consumed. Simulation results for new methods are considered.

“…We can find a good research on this at [19], where new methods are introduced. Another form of fast defuzzifiers is presented in [20].…”

Section: Defuzzificationmentioning

confidence: 99%

Discrete Fuzzy Inference Engine Algorithm for Digital Implementations of Approximate Reasoning

¹

,

²

,

³

et al. 2008

2008 Third International Conference on Convergence and Hybrid Information Technology

View full text Add to dashboard Cite

Fuzzy logic has become a very good choice to represent uncertain models of complex systems that cannot be easily represented in terms of conventional mathematics. Specifically fuzzy hardware has turned to be the choice to reach high speed inference rates. There are two forms to represent membership values universe: first one is when floating point numbers are used, second form is when integer universe is used. In the first case result of operations belongs to [0,1], in the second case results belong to interval of integers [0, m], where m is an integer that can be represented by a different number of bits according to application resolution demands. This case is fully compatible with digital computers because floating point operations consume much more time and resources than integer operations. This paper presents an algorithm to implement fuzzy logic inference engine for discrete implementations and new method of defuzzification procedure, with the objective of reducing the number of instructions to be executed, having as consequence fewer processing time and less resources consumed. Simulation results for new methods are considered.

Generalized Fuzzy Operations for Digital Hardware Implementation

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Camacho-Nieto³

et al.

MICAI 2007: Advances in Artificial Intelligence

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Computationally efficient active rule detection method: Algorithm and architecture

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et al. 2009

Fuzzy Sets and Systems

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No abstract

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