دانلود رایگان مقاله انگلیسی منطق فازی احتمالی و سیستم های فازی احتمالی - IEEE 2001

Abstract

A novel concept of probabilistic fuw logic is introduced as a way of representing and/or modeling existing randomness in many real world systems and natural language propositions. The approach is actually based on combining both the concepts of probability of truth and degree of truth in a unique framework. This combination is carried out in both the fuzzy sets and fuzzy rules resulting in the new concepts of probabilistic fiw sets and probabilistic fuzzy rules, respectively. Having one of these probabilistic elements, a probabilistic fuzqv system is then introduced as a fuzzy-probabilistic model of a complex non- deterministic system. In a simple example, human skepticism about the optimal fuzzy rule base is modeled through substituting a probabilistic fuzzy rule base for a conventional one. The closed loop response of the resulting controller for tank level control is shown through simulation and is compared with a conventional fuzzy controller.

5. SUMMARY

There is very little absolute knowledge in the real world. Inquiry must be a process of removal of doubt and skepticism or representing it properly. One of the main aspects of this skepticism has been identified as fuzziness, which is well represented with conventional fuzzy logic. Another aspect of the existing skepticism is uncertainty about the future of random processes and probabilistic events. Many of the real world systems, although may not have a random nature, may seem random to us due to insufficient knowledge and should be modeled accordingly. Although these two aspects have, up until now, been studied separately, the new concept of probabilistic fuzzy logic tries to merge them in a unifying framework. Probabilistic Jiczzy logic is a new approach for incorporating probability in fuzzy logic in order to better represent non-deterministic real world systems. It has not only the advantages of the approximate reasoning property of fuzzy systems, but also can be regarded as an extension to conventional one in the sense that the latter is a special case of the former with zero degree of randomness. The existing randomness in a probabilistic fuzzy system can be interpreted as: 1. Probabilistic nature in some of the natural language propositions and human reasoning as well as the statistical differences and variety of different human experts’ knowledge. 2. Existing randomness in many of the real world systems, which is required to be modeled, including human skepticism in defining the filzn knowledge base.