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An Improved Fault-Tolerant Objective Function and Learning Algorithm for Training the Radial Basis Function Neural Network
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DOI：10.1007/s12559-013-9236-x.png)
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As the concept of artificial neural networks is based on the mechanism of the human brain, it is essential that a trained artificial neural network should exhibit certain amount of fault-tolerant ability. In this paper, we propose a fault-tolerant learning method for training radial basis function (RBF) networks that may contain the coexistence of the stuck-at-zero node fault and the stuck-at-one node fault. First, we provide a formulation for evaluating the mean square error of the faulty RBF networks. Next an objective function, together with an algorithm for training the fault-tolerant RBF networks, is developed. Subsequently, we derive a mean prediction error (MPE) formula to estimate the test set error of the faulty RBF networks. With the MPE formula, we can estimate the RBF width that leads to near-optimal fault-tolerant capability. Finally, simulations are conducted to demonstrate the feasibility of our method, as well as its compliance with the theoretical outcome.
Keyword:
Fault tolerance
Generalization ability
Radial basis function
Regularization
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