کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4947178 | 1439567 | 2017 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A convergent smoothing algorithm for training maxâ-minâ fuzzy neural networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
In this paper, a smooth function is constructed to approximate the nonsmooth output of maxâ-minâ fuzzy neural networks (FNNs) and its approximation is also presented. In place of the output of maxâ-minâ FNNs by its smoothing approximation function, the error function, defining the discrepancy between the actual outputs and desired outputs of maxâ-minâ FNNs, becomes a continuously differentiable function. Then, a smoothing gradient decent-based algorithm with Armijo-Goldstein step size rule is formulated to train maxâ-minâ FNNs. Based on the existing convergent result, the convergence of our proposed algorithm can easily be obtained. Furthermore, the proposed algorithm also provides a feasible procedure to solve fuzzy relational equations with maxâ-minâcomposition. Finally, some numerical examples are implemented to support our results and demonstrate that the proposed smoothing algorithm has better learning performance than other two gradient decent-based algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 260, 18 October 2017, Pages 404-410
Journal: Neurocomputing - Volume 260, 18 October 2017, Pages 404-410
نویسندگان
Long Li, Zhijun Qiao, Yan Liu, Yuan Chen,