Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

Journal of Intelligent Manufacturing · Ağustos 2010

YÖKSİS Kayıtları

Journal of Intelligent Manufacturing · 2010 SCI-Expanded

Prof. Dr. MEHMET ÇUNKAŞ →

YÖKSİS Kayıtları — ISSN Eşleşmesi

Intelligent design of induction motors by multiobjective fuzzy genetic algorithm

2010 ISSN: 0956-5515 SCI-Expanded

Prof. Dr. MEHMET ÇUNKAŞ →

10.1007/s10845-008-0187-0

Parallel inference in dynamic decision support systems of a semiotic type

2004 ISSN: 0956-5515 SCI-Expanded

Doç. Dr. KEMAL TÜTÜNCÜ →

10.1023/B:JIMS.0000034113.86022.bd

Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation

2009 ISSN: 0956-5515 SCI-Expanded 18 atıf Q1

Prof. Dr. İSMAİL SARITAŞ →

10.1007/s10845-008-0226-x

Determination of the drug dose by fuzzy expert system in treatment of chronic intestine inflammation

2009 ISSN: 0956-5515 SCI-Expanded

Doç. Dr. İLKER ALİ ÖZKAN →

Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review

2023 ISSN: 0956-5515 SCI-Expanded Q1

Doç. Dr. MUSTAFA KUNTOĞLU →

10.1007/s10845-022-01923-2

Makale Bilgileri

Dergi Journal of Intelligent Manufacturing

ISSN09565515

Yayın TarihiAğustos 2010

Cilt / Sayfa21 · 393-402

DOI10.1007/s10845-008-0187-0

Scopus ID2-s2.0-77955653520

Özet In this paper an approach using multi-objective fuzzy genetic algorithm (MFGA) for optimum design of induction motors is presented. Single-objective genetic algorithm optimization is compared with the MFGA optimization. The efficiency of those algorithms is investigated on motor's performance. The comparison results show that MFGA is able to find more compromise solutions and is promising for providing the optimum design. Besides, a design tool is developed to evaluate and analysis the steady-state characteristics of induction motors. © 2008 Springer Science+Business Media, LLC.