A fuzzy modeling method using fuzzy neural networks with the backpropagation algorithm is presented. The method can identify the fuzzy model of a nonlinear system automatically. The feasibility of the method is examined using simple numerical data.
SUMMARYThis paper introduces an application of iris recognition technology, using the iris pattern for horse identification. There are several problems to be solved in horse iris recognition: (1) It is very difficult for horses to remain motionless, which leads to mislocation and loss of focus during image acquisition, so that the images often have poor quality. (2) Pupil size varies significantly between the conditions of dilation (mydriasis) and contraction (miosis). (3) Horse iris patterns are not clear. As a solution for issue (1), we used the reflection of the illumination sources employed for image acquisition and chose adequate images suitable for recognition. For issues (2) and (3), we propose region extraction appropriate to the equine eye structure, a stable coordinate model for pupil variation, and recognition using orthogonal wrinkles in the iris pattern. Recognition experiments based on 100 sets of horse iris data show that highly accurate horse identification is possible.
Fuzzy control has a distinctive feature in that it can incorporate experts' control rules using linguistic expressions. The authors have presented various types of fuzzy neural networks (FNNs) called Type I-V. The FNNs can automatically identify the fuzzy rules and tune the membership functions of fuzzy controllers by utilizing the learning capability of neural networks. In particular, the Type IV FNN has a simple structure and can express the identified fuzzy rules linguistically. The authors have also proposed a method to describe the behavior of fuzzy control systems based on the fuzzy models. The method can comprehensively express the dynamic behavior of fuzzy control systems and makes easy to know how to modify the fuzzy controllers. This paper studies an acquisition of fuzzy controller for an inverted pendulum using the Type IV FNNs and presents a new method for describing of the behavior of the fuzzy control system. The new method expresses the dynamic ehavior of the fuzzy control system more clearly by incorporating the change of the output of the controlled object. This new rule-to-rule mapping method enables easy modification of the fuzzy control rules. The experimental results illustrate that the method is effective in designing the fuzzy controllers having good performance.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.