کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
412916 679688 2010 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A multi-objective memetic and hybrid methodology for optimizing the parameters and performance of artificial neural networks
چکیده انگلیسی

The use of artificial neural networks implies considerable time spent choosing a set of parameters that contribute toward improving the final performance. Initial weights, the amount of hidden nodes and layers, training algorithm rates and transfer functions are normally selected through a manual process of trial-and-error that often fails to find the best possible set of neural network parameters for a specific problem. This paper proposes an automatic search methodology for the optimization of the parameters and performance of neural networks relying on use of Evolution Strategies, Particle Swarm Optimization and concepts from Genetic Algorithms corresponding to the hybrid and global search module. There is also a module that refers to local searches, including the well-known Multilayer Perceptrons, Back-propagation and the Levenberg–Marquardt training algorithms. The methodology proposed here performs the search using the aforementioned parameters in an attempt to optimize the networks and performance. Experiments were performed and the results proved the proposed method to be better than trial-and-error and other methods found in the literature.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 73, Issues 7–9, March 2010, Pages 1438–1450
نویسندگان
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