Fault immunization concept for self-organizing mapping neural networks
Self-Organizing Mapping (SOM) neural network has been widely used in pattern classification, vector quantization, and image compression. We consider the problem of strengthening the reliability of a SOM neural network by the technique of fault immunization of the synaptic links of each neuron which is similar to the concept of biological immunization. Instead of assuming the stuck-at-0 and stuck-at-1 as in those studies, we consider a general case of stuck-at-a, where a is a real value. The only assumption that we consider is only one neuron can be faulty at any time. No restriction on the number of faulty links of the neuron. Let w(i,j) be the weight of synaptic link j of neuron i obtained after the winner-take-all classification. Weight w(i,j) is immunized by adding a constant epsilon(i,j), either positive or negative, to w(i,j). A neuron reaches its maximum fault immunization if the value of w(i,j) + epsilon(i,j) can be either increased or decreased as much as possible without creating any misclassification. Thus, the fault immunization problem is formulated as an optimization problem on finding the value of each epsilon(i,j). A technique to find the value of w(i,j) + epsilon(i,j) is proposed and its application to enhance the transmission reliability in image compression area is introduced.
fault immunization self-organizing mapping competitive learning