Polynomial Kernel Function and its Application in Locally Polynomial Neurofuzzy Models
سال انتشار: 1388
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 2,102
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شناسه ملی سند علمی:
CSICC14_052
تاریخ نمایه سازی: 24 خرداد 1388
چکیده مقاله:
Polynomials are one of the most powerful functions that have been used in many fields of mathematics such as curve fitting and regression. Low order polynomials are desired for their smoothness1, good local approximation and interpolation. Being smooth, they can be used to locally approximate almost any derivable function. This means that when linear functions fail in approximation (e.g. where the first order Taylor expansion equals zero) polynomial functions can be used in local approximation, such that one can achieve better estimations at extremums. In this paper, application of polynomial kernel functions in locally linear neurofuzzy models is shown. Using polynomial kernels in local models, better local approximations in prediction of chaotic time series such as Mackey-Glass is achieved, and the capability of the neurofuzzy network is enhanced.
نویسندگان
A Shirvani
Islamic Azad University of Tehran Science and Research Branch/Computer Engineering Department, Iran
H Chegini
Islamic Azad University of Tehran Science and Research Branch/Computer Engineering Department, Iran
S Setayeshi
Amirkabir University of Technology/Faculty of Nuclear Engineering and Physics, Tehran, Iran
C Lucas
University of Tehran/Electrical and Computer Engineering Department, Control and Intelligent Processing Center of Excellence, Tehran, Iran.