Chip design of fuzzy neural networks for face recognition in mobile-robots

Fuzzy neural networks (FNN) have been successfully applied to classification problems. In this study, we design a FNN-based chip to achieve the face recognition of mobile-robots. The underlying notion of the proposed FNN is to split the generation of fuzzy rules into linear discriminant analysis (LDA) and Gaussian mixture model (GMM). In LDA, the weights are updated by seeking directions that are efficient for discrimination. In GMM, the parameter learning adopts the gradient descent method to reduce the cost function. The major contribution of this paper is to propose the hardware architecture of FNN chip. Furthermore, it has been fabricated in UMC 90nm technology. Since LDA-derived fuzzy rules increase the discriminative capability among different classes, the proposed FNN chip can classify highly confusable patterns.