The interest in neuro{fuzzy systems has grown tremendously over the last few years. First approaches concentrated mainly on neuro{fuzzy controllers, whereas newer approaches can also be found in the domain of data analysis. After successful applications in Japan neuro{fuzzy concepts also nd their way into the European industries, though mainly simple models, like FAMs, still prevail. This paper...

Load sensor is developed using fuzzy logic as well as neuro-fuzzy method. It is two inputs and one output sensor. Both fuzzy logic and neuro-fuzzy algorithms are simulated using MATLAB fuzzy logic toolbox. This paper outlines the basic difference between the results of fuzzy logic and neuro-fuzzy algorithms and provides the better algorithm for load sensor. Index Terms —fuzzy logic, load sensor...

Problem statement: In this study, we present the development of genetic algorithm based neuro fuzzy technique for process grain sized in scheduling of parallel jobs with the help of real lIfe workload data. Approach: The study uses the rule based scheduling strategy for the scheduling and classIfies all possible scheduling strategies. The rule bases are developed with the help of the neuro fuzz...

It is known that neuro-fuzzy system is easily stuck in local minimum. To improve these drawbacks, a two-stage algorithm combining the advantages of neuro-fuzzy and genetic algorithms (GA) is integrated in system identification. Genetic algorithms are general purposed optimization algorithms with adaptive reproduction, crossover, and mutation operators that provide a method to search optimal par...

This paper focuses on hardware implementations of fuzzy inference systems which provide low silicon cost, high operational speed and adaptability to different application domains. The architecture and basic building blocks of two fuzzy logic controllers are described and their functionality is illustrated with experimental results showing the capability of these systems to be applied as functio...

In this paper, the idea of the neuro-fuzzy learning algorithm has been extended, by which the tuning parameters in the fuzzy rules can be learned without changing the fuzzy rule table form used in usual fuzzy applications. A new neuro-fuzzy learning algorithm in the case of the fuzzy singleton-type reasoning method has been proposed. Due to the flexibility of the fuzzy singleton-type reasoning ...

Recently, through the use of parameterized fuzzy approximators, various adaptive fuzzy control schemes have been developed to deal with nonlinear systems whose dynamics are poorly understood. An important class of parameterized fuzzy approximators is constructed using radial basis function (RBF) as a membership function. However, some tuneable parameters in RBF appear nonlinearly and the determ...

This paper reviews neuro-fuzzy systems, which combine methods from neural network theory with fuzzy systems. Such combinations have been considered for several years already. However, the term neuro-fuzzy still lacks proper deenition, and still has the avour of a buzzword to it. Surprisingly few neuro-fuzzy approaches do actually employ neural networks, even though they are very often depicted ...

This paper presents a new hybrid neuro-fuzzy model which is capable of learning structure and parameters by means of recursive binary space partitioning BSP. Introduction Neuro-fuzzy systems (NFSs) [1] combine the learning ability of artificial neural nets (ANNs) with the linguistic interpretation capacity of fuzzy inference systems (FISs) [2]. This work makes use of BSP (Binary Space Partition...

Fuzzy technology became a very important controlling method in complex systems where traditional methods are unsuccessful. It was proved in [19] that fuzzy rule systems can be used as general approximators of any complex continuous systems. The key element of the approximation process is the construction of the corresponding fuzzy rule system that encapsulates the knowledge on the problem domai...