Qualitative modeling may be applied when knowledge about a system is only available in linguistic form. The knowledge might be processed by a dynamic fuzzy system consisting of a rule base and an inference method modeling human reasoning. Conventional fuzzy inference methods do not consider this association to human reasoning and therefore are not suitable for the dynamic processing of linguist...

Fuzzy modeling generally comprises structure identification and parameter identification. The former determines the structure of a rule-base, whereas the latter determines the contents of each rule. Applying neural networks or genetic algorithms to identify the parameter sets and structures of a fuzzy system is increasingly popular owing to their ability to learn and adapt. However, most conven...

The survey and modeling of the deformations of large structures is a major task in engineering geodesy. In this paper, a new procedure to describe and predict the deformations is presented and discussed which is based on Neuro-Fuzzy modeling. Neuro-Fuzzy methods are data driven; they deduce the model directly from the data. Hence, they are mostly convenient if there are no physical models avail...

In many practical applications, it turns out to be useful to use the notion of fuzzy transform: once we have functions A1(x) ≥ 0, . . . , An ≥ 0, with n ∑ i=1 Ai(x) = 1, we can then represent each function f(x) by the coefficients Fi = ∫ f(x) ·Ai(x) dx ∫ Ai(x) dx . Once we know the coefficients Fi, we can (approximately) reconstruct the original function f(x) as n ∑ i=1 Fi · Ai(x). The original...

Most processes in industry are characterized by nonlinear and time-varying behavior. Nonlinear system identification is becoming an important tool which can be used to improve control performance and achieve robust fault-tolerant behavior. Among the different nonlinear identification techniques, methods based on neuro-fuzzy models are gradually becoming established not only in the academia but ...

This paper briefly elaborates on a development in (applied) fuzzy logic that has taken place in the last couple of decades, namely, the complementation or even replacement of the traditional knowledge-based approach to fuzzy rule-based systems design by a data-driven one. It is argued that the classical rule-based modeling paradigm is actually more amenable to the knowledge-based approach, for ...

One of the important challenges in Graphical models is the problem of dealing with the uncertainties in the problem. Among graphical networks, fuzzy cognitive map is only capable of modeling fuzzy uncertainty and the Bayesian network is only capable of modeling probabilistic uncertainty. In many real issues, we are faced with both fuzzy and probabilistic uncertainties. In these cases, the propo...

Welcome to the Journal of Intelligent and Fuzzy Systems’s special issue for “FUZZYSS’2009: 1st International Fuzzy Systems Symposium”. Since its introduction by Prof. Lotfi A. Zadeh fuzzy logic has found numerous applications in many diverse areas. Scientific research related to fuzzy logic and its extensions is still an open and promising active research area. There is still a very long way to...

Fuzzy modeling of high-dimensional systems is a challenging topic. This paper proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. An initial fuzzy rule system is generated based on the conclusion that optimal fuzzy rules cover extrema [8]. Redundant rules are removed based on a fuzzy similarity measure. Then, the structure and parameters of the fuzzy system ...

Fuzzy linear symmetrical bi-level programming is the most extensive problem in multi-level programming. A new method based on tolerance degree has been introduced in this paper. The method mainly concerns the modeling of complicated Supply Chain with bi-level Stackelberg structure. We analyze the reason lead to uncertainties in supply chain, summarize methods of dealing with uncertainties, and ...