Feedback analysis of radial basis functions neural networks via small gain theorem

S. Saad Azhar Ali, Muhammad Shafiq, Jamil M. Ba-Khashwain, Fouad M. Al-Sunni

نتاج البحث

ملخص

Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control, time series prediction, etc. In this paper, feedback analysis of the learning algorithm of radial basis function neural networks is presented. It studies the robustness of the learning algorithm in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch. The learning scheme is first associated with a feedback structure and then the stability of that feedback structure is analyzed via small gain theorem. The analysis suggests bounds on the learning rate in order to guarantee that the learning algorithm will behave as robust nonlinear filters and optimal choices for faster convergence speeds.

اللغة الأصليةEnglish
عنوان منشور المضيفProceedings of the 17th World Congress, International Federation of Automatic Control, IFAC
طبعة1 PART 1
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2008
الحدث17th World Congress, International Federation of Automatic Control, IFAC - Seoul
المدة: يوليو ٦ ٢٠٠٨يوليو ١١ ٢٠٠٨

سلسلة المنشورات

الاسمIFAC Proceedings Volumes (IFAC-PapersOnline)
الرقم1 PART 1
مستوى الصوت17
رقم المعيار الدولي للدوريات (المطبوع)1474-6670

Other

Other17th World Congress, International Federation of Automatic Control, IFAC
الدولة/الإقليمKorea, Republic of
المدينةSeoul
المدة٧/٦/٠٨٧/١١/٠٨

ASJC Scopus subject areas

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