Fault Tolerance Analysis of Neural Networks for Pattern Recognition

Interest in the area of pattern recognition has been renewed recently due to emerging applications which are not only challenging but also computationally more demanding. These applications include data mining (identifying a "pattern", e.g., correlation, or an outlier in millions of multidimensional patterns), document classifications (efficiently searching text documents), organization and retrieval of multimedia database, and biometrics (personal identifications based on various physical attributes such as face and fingerprints). The three best known conventional approaches for pattern recognition are: template matching, statistical classification and syntactic or structural matching. The limitations and constraints of these conventional approaches have made researchers to look for alternate techniques based on Artificial Neural Networks. The main characteristics of neural networks are that they have the ability to learn complex nonlinear input-output relationship, use sequential training procedures, and adapt themselves to the data. In this paper, we discuss implementation and fault tolerance analysis of the most commonly used family of neural networks for pattern classification tasks i.e., the feed forward network, which includes a fully interconnected three layered [25-10-1] perceptron. The delta rule weight adjustment is implemented by taking the gradient of error function, which gives the direction in which weights have to be adjusted to get error value within the predefined threshold. Fault tolerance analysis is done for Gaussian and Uniform distribution of weights. The efficacy of neural network based pattern recognition is tested by the computer simulation results.