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advances in artificial neural networks – methodological development and application人工神经网络的进步u2014u2014方法论的发展和应用
Algorithms 2009, 2, 973-1007; doi:10.3390/algor2030973
OPEN ACCESS
algorithms
ISSN 1999-4893
/journal/algorithms
Review
Advances in Artificial Neural Networks – Methodological
Development and Application
Yanbo Huang
United States Department of Agriculture, Agricultural Research Service, Application and Production
Technology Research Unit, 141 Experiment Station Road, Stoneville, Mississippi 38776, USA;
E-Mail: yanbo.huang@; Tel. +1 (662) 686-5354; Fax: +1 (662) 686-5372
Received: 1 July 2009; in revised form: 24 July 2009 / Accepted: 28 July 2009 /
Published: 3 August 2009
Abstract: Artificial neural networks as a major soft-computing technology have been
extensively studied and applied during the last three decades. Research on backpropagation
training algorithms for multilayer perceptron networks has spurred development of other
neural network training algorithms for other networks such as radial basis function,
recurrent network, feedback network, and unsupervised Kohonen self-organizing network.
These networks, especially the multilayer perceptron network with a backpropagation
training algorithm, have gained recognition in research and applications in various
scientific and engineering areas. In order to accelerate the training process and overcome
data over-fitting, research has been conducted to improve the backpropagation algorithm.
Further, artificial neural networks have been integrated with other advanced methods suc
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