A neuro-fuzzy system is the combined the advance feature of fuzzy logic and neural network, it is simply a fuzzy inference system that is trained by the learning concept of neural network. In NFS learning mechanism fine-tunes the underlying fuzzy inference system. This paper presents fundamental concepts and parameterized comparison in the aspects of fuzzy logic, neural network and neuro-fuzzy ...

In this work, we show that fuzzy inference systems based on Similarity Based Reasoning (SBR) where the modification function is a fuzzy implication is a universal approximator under suitable conditions on the other components of the fuzzy system.

Abtract: The construction of fuzzy rule-based classification systems with both good generalization ability and interpretability is a chalenging issue. The paper aims to present a novel framework for the realization of these important (and many times conflicting) goals simultaneously. The generalization performance is obtained with the adaptation of Support Vector algorithms for the identificati...

New methods, like fuzzy logic, are coming into the field of adaptive traffic signal control. Development of the fuzzy control can roughly be divided into two research approaches: development of fuzzy control functions, and development of fuzzy inference methods. Both approaches are discussed in this paper. First, a lately developed fuzzy inference method, called maximal fuzzy similarity, is int...

This article presents the development of the knowledge base and inference motor for an Automated Management System for developing Expert Systems and Fuzzy Classifier (SAGSECD). SAGSECD is a tool that can be used for any user with basics knowledge in the expert systems or fuzzy classifiers area for designing and implementing this kind of systems to solve several problems or situations that requi...

Rule-driven processing is a proven way of achieving high-speed in fuzzy processing. Up to now, ruledriven architectures where designed to work with minimum or product as T-norm. Nevertheless, a Lukasiewicz T-norm is typically used with the compositional rule of inference in expert systems applications that are based on a fuzzy inference engine. This paper presents a rule-driven processing archi...

Causal networks (CNs) have been used to construct inference systems for diagnostics and decision making. More recently, Bayesian causal networks (BCNs) and fuzzy causal networks (FCNs) have gained considerable attention and offer an alternative framework for representing structured human knowledge and are used in causal inference in many real-world applications. However, for large systems, it i...

The fuzzy logic is effectively connected in different applications in all fields of engineering and science including buyer gadgets, control systems, signal and image processing etc.The different fuzzy processing systems have been utilized by distinctive specialists for advancement of different applications. The focal point of fuzzy systems is to approximate system behavior where numerical rela...

Gas turbines are currently a popular power generation technology in countries with access to natural gas resources. However they are very complex systems the operation of which at peak performance is challenging. This paper proposes the use of a hybrid approach based on an Adaptive Neuro-Fuzzy Inference System (ANFIS) for the control of the speed and the exhaust temperature of a gas turbine. Th...

Inference analysis plays a major role in database security and knowledge discovery. Common sense knowledge, typically expressed in imprecise or fuzzy terms, can be introduced as catalytic relations to existing databases. Analyzing the augmented databases materializes new rules and latent compromising inference channels based on common knowledge and existing database data. This paper shows how f...