کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
381136 1437475 2011 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Design of information granule-oriented RBF neural networks and its application to power supply for high-field magnet
چکیده انگلیسی

To realize effective modeling and secure accurate prediction abilities of models for power supply for high-field magnet (PSHFM), we develop a comprehensive design methodology of information granule-oriented radial basis function (RBF) neural networks. The proposed network comes with a collection of radial basis functions, which are structurally as well as parametrically optimized with the aid of information granulation and genetic algorithm. The structure of the information granule-oriented RBF neural networks invokes two types of clustering methods such as K-Means and fuzzy C-Means (FCM). The taxonomy of the resulting information granules relates to the format of the activation functions of the receptive fields used in RBF neural networks. The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., the width of the Gaussian function, the numbers of nodes in the hidden layer, and a fuzzification coefficient used in the FCM method). During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed model is applied to modeling power supply for high-field magnet where the model is developed in the presence of a limited dataset (where the small size of the data is implied by high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. The obtained experimental results show that the proposed network exhibits high accuracy and generalization capabilities.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 24, Issue 3, April 2011, Pages 543–554
نویسندگان
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