Training RBF network to tolerate single node fault

In this paper, an objective function for training a radial basis function (RBF) network to handle single node open fault is presented. Based on the definition of this objective function, we propose a training method in which the computational complexity is the same as that of the least mean squares (LMS) method. Simulation results indicate that our method could greatly improve the fault tolerance of RBF networks, as compared with the one trained by LMS method. Moreover, even if the tuning parameter is misspecified, the performance deviation is not significant.

[1]  Sheng Chen,et al.  Local regularization assisted orthogonal least squares regression , 2006, Neurocomputing.

[2]  Dhananjay S. Phatak,et al.  Complete and partial fault tolerance of feedforward neural nets , 1995, IEEE Trans. Neural Networks.

[3]  A Learning Algorithm for Fault Tolerant Feedforward Neural Networks , 1996 .

[4]  Zhi-Hua Zhou,et al.  Evolving Fault-Tolerant Neural Networks , 2003, Neural Computing & Applications.

[5]  Michael T. Manry,et al.  LMS learning algorithms: misconceptions and new results on converence , 2000, IEEE Trans. Neural Networks Learn. Syst..

[6]  Zhi-Hua Zhou,et al.  Improving tolerance of neural networks against multi-node open fault , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[7]  Salvatore Cavalieri,et al.  A novel learning algorithm which improves the partial fault tolerance of multilayer neural networks , 1999, Neural Networks.

[8]  N. Kamiura.  On a Weight Limit Approach for Enhancing Fault Tolerance of Feedforward Neural Networks , 2000 .

[9]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[10]  Andrew Chi-Sing Leung,et al.  On Objective Function, Regularizer, and Prediction Error of a Learning Algorithm for Dealing With Multiplicative Weight Noise , 2009, IEEE Transactions on Neural Networks.

[11]  Yunqian Ma,et al.  Multiple model regression estimation , 2005, IEEE Transactions on Neural Networks.

[12]  Chalapathy Neti,et al.  Maximally fault tolerant neural networks , 1992, IEEE Trans. Neural Networks.

[13]  Chilukuri K. Mohan,et al.  Modifying training algorithms for improved fault tolerance , 1994, Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94).

[14]  Robert I. Damper,et al.  Determining and improving the fault tolerance of multilayer perceptrons in a pattern-recognition application , 1993, IEEE Trans. Neural Networks.

[15]  Dan Simon,et al.  Fault-tolerant training for optimal interpolative nets , 1995, IEEE Trans. Neural Networks.

[16]  国家科学委員会.  Proceedings of the National Science Council, Republic of China , 1969 .

[17]  Dan Simon,et al.  Distributed fault tolerance in optimal interpolative nets , 2001, IEEE Trans. Neural Networks.