Direct Estimation of Fault Tolerance of Feed Forward Neural Networks in Pattern Recognition

This paper studies fault-tolerance problem of feed forward neural networks implemented in pattern recognition. Based on dynamical system theory, two concepts of pseudo-attractor and its region of attraction are introduced. A method estimating fault tolerance of feed forward neural networks has been developed. This paper also presents definitions of terminologies and detailed derivations of the methodology. Some preliminary results of case studies using the proposed method are shown. Comparing to traditional methods, the proposed method has provided a framework and an efficient way for direct evaluation of fault-tolerance in feed forward neural networks.