A hardware oriented fuzzification algorithm and its VLSI implementation
An efficient fuzzification algorithm named as Dynamic Precision Fuzzification (DPF) is introduced in this paper which is mainly developed for hardware implementation. The DPF which might be generally used with any piecewise linear membership function, exploits an inherent capacity of the normal fuzz...
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Veröffentlicht in: | Soft computing (Berlin, Germany) Germany), 2013-04, Vol.17 (4), p.683-690 |
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creator | Haji Seyed Javadi, Mohammad Mahdiani, Hamid Reza Zeinali Kh, Esmaeil |
description | An efficient fuzzification algorithm named as Dynamic Precision Fuzzification (DPF) is introduced in this paper which is mainly developed for hardware implementation. The DPF which might be generally used with any piecewise linear membership function, exploits an inherent capacity of the normal fuzzification algorithm to improve its efficiency when realized in a finite-precision implementation bed such as digital VLSI. The accuracy simulation results of the DPF and normal fuzzification method are presented and compared to show the superiority of the DPF. As the word-length is the most important parameter in a finite-precision implementation environment which determines the system cost-precision trade-off, the simulation results show that DPF provides suitable precision improvements with respect to traditional fuzzification without increasing the system word-length. The VLSI synthesis results of both methods are also presented to show that this considerable accuracy improvement is achieved by an acceptable increase in its VLSI implementation costs in terms of area, delay, and power consumption with respect to traditional methods. |
doi_str_mv | 10.1007/s00500-012-0940-3 |
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The DPF which might be generally used with any piecewise linear membership function, exploits an inherent capacity of the normal fuzzification algorithm to improve its efficiency when realized in a finite-precision implementation bed such as digital VLSI. The accuracy simulation results of the DPF and normal fuzzification method are presented and compared to show the superiority of the DPF. As the word-length is the most important parameter in a finite-precision implementation environment which determines the system cost-precision trade-off, the simulation results show that DPF provides suitable precision improvements with respect to traditional fuzzification without increasing the system word-length. 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The DPF which might be generally used with any piecewise linear membership function, exploits an inherent capacity of the normal fuzzification algorithm to improve its efficiency when realized in a finite-precision implementation bed such as digital VLSI. The accuracy simulation results of the DPF and normal fuzzification method are presented and compared to show the superiority of the DPF. As the word-length is the most important parameter in a finite-precision implementation environment which determines the system cost-precision trade-off, the simulation results show that DPF provides suitable precision improvements with respect to traditional fuzzification without increasing the system word-length. The VLSI synthesis results of both methods are also presented to show that this considerable accuracy improvement is achieved by an acceptable increase in its VLSI implementation costs in terms of area, delay, and power consumption with respect to traditional methods.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Computational Intelligence</subject><subject>Control</subject><subject>Engineering</subject><subject>Fuzzy sets</subject><subject>Hardware</subject><subject>Mathematical Logic and Foundations</subject><subject>Mechatronics</subject><subject>Original Paper</subject><subject>Power consumption</subject><subject>Robotics</subject><subject>Software</subject><issn>1432-7643</issn><issn>1433-7479</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kEtLAzEUhYMoWKs_wF3AdfTmMUlnWYrWQsGFj23ITJI2pTNTkylif71pR3Dl6h643zmXexC6pXBPAdRDAigACFBGoBRA-BkaUcE5UUKV5yfNiJKCX6KrlDYAjKqCj9B8itcm2i8THe5icG3vLPb7wyH4UJs-dC0221Xe9OsGm9bi0Cf8sXxd4NDstq7JhhN1jS682SZ38zvH6P3p8W32TJYv88VsuiQ1p7InlRF17WRZeemFdFA5ZtTEUSm5FaUFar0qqGVZ5DcK4LyqmahlZczEl5XhY3Q35O5i97l3qdebbh_bfFKzkqoSGBOQKTpQdexSis7rXQyNid-agj72pYe-dO5LH_vSPHvY4EmZbVcu_iX_b_oBzhJtmg</recordid><startdate>20130401</startdate><enddate>20130401</enddate><creator>Haji Seyed Javadi, Mohammad</creator><creator>Mahdiani, Hamid Reza</creator><creator>Zeinali Kh, Esmaeil</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20130401</creationdate><title>A hardware oriented fuzzification algorithm and its VLSI implementation</title><author>Haji Seyed Javadi, Mohammad ; 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The DPF which might be generally used with any piecewise linear membership function, exploits an inherent capacity of the normal fuzzification algorithm to improve its efficiency when realized in a finite-precision implementation bed such as digital VLSI. The accuracy simulation results of the DPF and normal fuzzification method are presented and compared to show the superiority of the DPF. As the word-length is the most important parameter in a finite-precision implementation environment which determines the system cost-precision trade-off, the simulation results show that DPF provides suitable precision improvements with respect to traditional fuzzification without increasing the system word-length. 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subjects | Accuracy Algorithms Artificial Intelligence Computational Intelligence Control Engineering Fuzzy sets Hardware Mathematical Logic and Foundations Mechatronics Original Paper Power consumption Robotics Software |
title | A hardware oriented fuzzification algorithm and its VLSI implementation |
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