This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the...

This paper investigates a hybrid methodology that combines fuzzy logic and neural networks, Fuzzy Cognitive Map (FCM), for modeling and controlling Supervisory Control Systems. A mathematical description of Fuzzy Cognitive Maps (FCM) will be presented and new construction methods will be extensively examined. A Fuzzy Cognitive Map will be developed to model and control a process example and the...

In sensory analysis a panel of assessors gives scores to blocks of sensory attributes for profiling products, thus yielding a three-way table crossing assessors, attributes and products. In this context, it is important to evaluate the panel performance as well as to synthesize the scores into a global assessment to investigate differences between products. Recently, a combined approach of fuzz...

In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

In the present paper Fuzzy process, similar to a stochastic process is carried out for reliability analysis of the network modeling. Classical reliability analysis carries out the probability and binary state assumption which has been found to be inadequate to handle uncertainties of failure data and modeling. In the present paper we have attempted to review the fuzzy tools when dealing with re...

Neuro-fuzzy computing, which provides efficient information processing capability by devising methodologies and algorithms for modeling uncertainty and imprecise information, forms at this juncture, a key component of soft computing. An integrated neuro-fuzzy system is simply a fuzzy inference system trained by a neural networklearning algorithm. The learning mechanism fine-tunes the underlying...

nowadays, the problem of the flexibility in any organization has essential importance and the associated investigation with the organizational efficiency and performance is considered. in real-world situation, the process of decision making is often based on linguistic and mental data, so applying fuzzy logic in modeling at this point of view is convenient. in this paper, a fuzzy approach is ap...

In the real world, data is imperfect and in various ways such as imprecision, vagueness, uncertainty, ambiguity and inconsistency. For geographic data, the fuzzy aspect is mainly manifested in time, space and the function of objects and is due to a lack of precision. Therefore, the researchers in the domain emphasize the importance of modeling data structures in GIS but also their lack of adapt...

in structural time seriesregression models, binary (classic) dummy variables are used to quantify the effects of economic structural breaks, changes or qualitative variables. the binary dummy variable can be challenged using this dummy variable in which 0 or 1 represent absence or presence of structural breaks(or changes), but presence of the structural breaks(or changes) may not have uniform e...

This research frame work investigates the application of a clustered based Neuro-fuzzy system to nonlinear dynamic system modeling from a set of input-output training patterns. It is concentrated on the modeling via Takagi-Sugeno (T-S) modeling technique and the employment of fuzzy clustering to generate suitable initial membership functions. Hence, such created initial memberships are then emp...