Fuzzy neural networks

Ambiguity is always present in any realistic process. This ambiguity may arise from the interpretation of the data inputs and in the rules used to describe the relationships between the informative attributes. Fuzzy logic provides an inference structure that enables the human reasoning capabilities to be applied to artificial knowledge-based systems. For efficient working the artificial knowledge-based systems depend upon algorithms which are cumbersome to implement and require extensive computational time. On the other hand, the human brain which performs approximate reasoning employs simple information processing elements called neurons. The paradigm of artificial neural networks, developed to emulate some of the capabilities of the human brain, has demonstrated a great potential in terms of learning and adaptation for various applications such as system identification and control, pattern recognition, prediction, etc. They provide low-level computations and embodies salient features such as learning, fault-tolerance, parallelism and generalization. On the other hand, fuzzy logic provides a means for converting linguistic strategy into control actions and thus offering a high-level computation. Although, fuzzy logic and artificial neural networks are both functionally and structurally different, it is envisaged that the synthesis of these two areas will give rise to a new paradigm called fuzzy neural networks. The latter have the potential to capture the benefits of both the fields, fuzzy logic and neural networks, into a single paradigm. The objective of this paper is to describe the basic concepts of fuzzy neural networks. Towards this goal, a fuzzy neural structure based on the notion of T-norm and T-conorm is developed. A fuzzy cellular neural network as applied to image enhancement is also described in this paper.