A new neural network Designing Approach for hysteresis modeling based on Preisach model
Publish Year: 1393
نوع سند: مقاله کنفرانسی
زبان: English
View: 781
This Paper With 7 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
ICEEE06_169
تاریخ نمایه سازی: 1 مهر 1394
Abstract:
One of the main issues in modeling the behaviorof electric machineries is the process of modeling themagnetic material used in such machineries. Almostall ferromagnetic materials display a set of behaviorswhich are known under the term of magnetichysteresis. Models which are proposed for thehysteresis phenomenon based on the physicalbehavior of ferromagnetic materials, such asPreisach, is so complicated which require largecomputer storage and also consume a lot of time forcalculation. Therefore, artificial neural networkscould be considered as suitable alternatives sincealong with featuring a high accuracy, they are fastand require less computer storage. Using multi-layerfeed-forward neural networks in the following paper,a model for magnetic hysteresis is proposed whichcould model all internal loops along with the mainhysteresis loop. Results to the simulation suggest agood agreement between the aforementioned modeland accurate model of Preisach
Keywords:
Hysteresis identification , Preisach model , neural network , perceptron , radial basis function (RBF)
Authors
Alireza Roosta
Shiraz University of Technology, Electrical and Electronics Engineering Department
Behrouz Safarinejadian
Shiraz University of Technology, Electrical and Electronics Engineering Department
Alireza Soltanimehr
Shiraz University of Technology, Electrical and Electronics Engineering Department
مراجع و منابع این Paper:
لیست زیر مراجع و منابع استفاده شده در این Paper را نمایش می دهد. این مراجع به صورت کاملا ماشینی و بر اساس هوش مصنوعی استخراج شده اند و لذا ممکن است دارای اشکالاتی باشند که به مرور زمان دقت استخراج این محتوا افزایش می یابد. مراجعی که مقالات مربوط به آنها در سیویلیکا نمایه شده و پیدا شده اند، به خود Paper لینک شده اند :