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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Brief Introduction of Back Propagation (BP) Neural Network Algorithm and Its Improvement
verfasst von : Jing Li, Ji-hang Cheng, Jing-yuan Shi, Fei Huang
Erschienen in: Advances in Computer Science and Information Engineering
Verlag: Springer Berlin Heidelberg
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The back propagation (BP) neural network algorithm is a multi-layer feedforward network trained according to error back propagation algorithm and is one of the most widely applied neural network models. BP network can be used to learn and store a great deal of mapping relations of input-output model, and no need to disclose in advance the mathematical equation that describes these mapping relations. Its learning rule is to adopt the steepest descent method in which the back propagation is used to regulate the weight value and threshold value of the network to achieve the minimum error sum of square. This paper focuses on the analysis of the characteristics and mathematical theory of BP neural network and also points out the shortcomings of BP algorithm as well as several methods for improvement.