بهبود رویکرد مدل‌سازی فازی Sugeno-Yasukawa با استفاده از روش خوشه‌بندی و تصویرسازی جدید برای تقسیم بندی فضای ورودی

نوع مقاله : علمی-پژوهشی

نویسنده

گروه کامپیوتر - دانشکده مهندسی برق و کامپیوتر

چکیده

الگوریتم های محاسباتی نرم مانند منطق فازی، شبکه های عصبی و الگوریتم های تکاملی به طور گسترده در بسیاری از زمینه ها استفاده می شوند. به ویژه منطق فازی، به دلیل توانایی قابل توجهی که در مدل سازی دارد، محبوبیت قابل توجهی به دست آورده است. تاکنون روش های مختلفی برای مدل سازی فازی ارائه شده است که هر یک از این روش ها مزایا و معایب خود را دارند. در حالی که اکثر روش ها تجزیه و تحلیل را از ورودی دارند ولی روش SY از بخش خروجی به تجزیه و تحلیل سیستم می پردازد. محبوبیت روش SY را می توان به الگوریتم استخراج قانون موثر آن نسبت داد، که از یک فرآیند خوشه بندی برای تعیین توابع عضویت ورودی استفاده می کند. در این مقاله، یک الگوریتم جستجوی خوشه‌ای و یک روش تقسیم بندی فازی جدید پیشنهاد شده است که نگاشت فضای خروجی به فضای ورودی را با توزیع توابع گاوسی برای هر نقطه داده در یک خوشه و محاسبه مقادیر عضویت آن‌ها افزایش می‌دهد. با این روش جدید جستجوی خوشه‌بندی پیشنهادی، عملکرد روش SY بهبود یافته است. با توجه به شبیه‌سازی ها، روش پیشنهادی معیار میانگین مربعات خطا (MSE) را 0.001 و معیار دقت را 1.5 درصد بهبود بخشیده است.

کلیدواژه‌ها

عنوان مقاله [English]

ISY: Improved Sugeno-Yasukawa Fuzzy Modelling Approach Using a Novel Clustering and Project Method for Input Partitioning

نویسنده [English]

  • S. Haghzad Klidbary
Department Electrical and Computer Engineering, University of Zanjan, Zanjan, Iran.
چکیده [English]

Soft computing algorithm such as fuzzy logic, neural networks, and evolutionary algorithms are widely used in many fields. Fuzzy logic, in particular, has gained significant popularity due to its significant ability in modelling. So far, various methods of fuzzy modelling have been presented; each of these methods has its advantages and disadvantages. While all methods start from the input, Sugeno-Yasukawa (SY) differs by initiating the analysis from the output. The popularity of the SY method can be attributed to its effective rule extraction algorithm, which employs a clustering process to determine input membership functions. In this paper, we propose a cluster search algorithm and a new fuzzy partitioning method that enhance the mapping of the output space to the input space by distributing Gaussian functions for each data point within a cluster and calculating their membership values. With this proposed new clustering search method, the performance of the SY method is improved. Through simulations, the proposed method has improved the mean square errors (MSE) criterion by 0.001, and improved the accuracy criterion by 1.5 percent.

کلیدواژه‌ها [English]

  • Fuzzy Inference System (FIS)
  • Fuzzy Modelling
  • Sugeno-Yasukawa (SY) Method
  • Membership Function Approximation
  • Clustering

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