The RBF Neural Network Based on Kalman Filter Algorithm and Dual Radial Transfer Function

Shao Yun Song; Bao Hua Zhang; Yu Ma

doi:10.4028/www.scientific.net/AMR.971-973.1816

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 971-973The RBF Neural Network Based on Kalman Filter...

The RBF Neural Network Based on Kalman Filter Algorithm and Dual Radial Transfer Function

Abstract:

RBF neural network have advantages of training simple, fast efficiency of learning, easy to fall into local minima, etc..It is widely used to solve the problem in signal processing and pattern recognition. Although the common RBF network is relatively easy to build, but because of the structure is usually fixed or high complexity, resulting in learning time is too long or network resource waste. For these reasons, proposed using extended Kalman filter as the RBF learning algorithm, and using double radial function in the hidden layer. By approaching the basis of the results of the analysis clearly shows that the network model than the other categories have a stronger generalization.

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Periodical:

Advanced Materials Research (Volumes 971-973)

Pages:

1816-1819

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.971-973.1816

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Online since:

June 2014

Authors:

Shao Yun Song*, Bao Hua Zhang, Yu Ma

Keywords:

Kalman Filter, Radial Basis Function (RBF), RBF Neural Network, Transfer Function

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* - Corresponding Author

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