Fault Tolerance Comparison of IDS Models with Multilayer Perceptron and Radial Basis Function Networks

The ink drop spread (IDS) method is a modeling technique developed by algorithmically mimicking the information-handling processes of the human brain. This method is a new paradigm of soft computing. The structure of IDS models is similar to that of artificial neural networks: they comprise distributed processing units. The beneficial property of fault tolerance is obtained when such parallel processing networks are implemented with dedicated hardware. This paper compares the IDS models with multilayer perceptron and radial basis function networks in terms of fault tolerance. The experimental results based on stuck-at fault tests reveal that the IDS models possess good fault tolerance.