طراحی الگوریتم تشخیص چهره با استفاده از ترکیب PCA -LDA برای شبکه های عصبی RBF مبتنی بر چند جمله ای و پیش پردازش داده های ترکیبی : طراحی و کاربرد آن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|70459||2013||16 صفحه PDF||سفارش دهید||محاسبه نشده|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 40, Issue 5, April 2013, Pages 1451–1466
The proposed P-RBF NNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part realized in terms of fuzzy “if–then” rules. In the condition part of fuzzy rules, the input space is partitioned with the use of fuzzy clustering realized by means of the Fuzzy C-Means (FCM) algorithm. In the conclusion part of rules, the connection weight is realized through three types of polynomials such as constant, linear, and quadratic. The coefficients of the P-RBF NNs model are obtained by fuzzy inference method forming the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum, fuzzification coefficient, and the feature selection mechanism) of the networks are optimized by means of differential evolution (DE). The experimental results completed on benchmark face datasets – the AT&T, and Yale datasets demonstrate the effectiveness and efficiency of PCA-LDA combined algorithm compared with other algorithms such as PCA, LPP, 2D-PCA and 2D-LPP. A real time face recognition system realized in this way is also presented.