In this paper we present the application of Concurrent Fuzzy Prolog (ConFuP) 10, 9] in the eld of modeling exible manufacturing systems. ConFuP ooers the feasibility of concurrent process modeling through a set of descriptive rules based on uncertain or qualitative knowledge and enables the user to model hierarchical descriptions from a physical point of view (e.g. machines, tools, etc.) as wel...

In this paper, an approach for modeling and analysis of time critical, dynamic and complex systems using stochastic Petri nets together with fuzzy sets is presented. The presented method consists of two stages. The first stage is same as the conventional stochastic Petri nets with the difference that the steady-state probabilities are obtained parametrically in terms of transition firing rates....

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The increasing complexity and tight coupling between people and computer systems in military operations has led to improved efficiency, as well as greater vulnerability due to system failure. Careful management of workflow systems can minimize operational vulnerability in command and control. Tavana et al. (2011) developed a workflow management framework capable of both modeling structure and p...

In this paper the design and development of a hierarchical fuzzy logic Systems are investigated. A new method using genetic algorithms for design of hierarchical fuzzy logic systems are proposed. This research study is unique in the way proposed method is applied to design and development of hierarchical fuzzy logic systems. The proposed method is then applied to financial modelling and predict...

Fuzziness is an important aspect of the uncertainties of spatial data. In this paper, the mechanism and methods of spatial modeling for fuzzy geographical objects are focused and the following two issues are resolved: (1) Modeling fuzzy geographical objects; (2) Modeling the relation between two fuzzy geographical objects. In the first issue, fuzzy geographical objects are defined by the novel ...

Abstract A novel adaptive fuzzy controller for a class of uncertain multivariable nonlinear systems with time delays is proposed in this paper. By combining fuzzy Takagi-Sugeno (T-S) models and fuzzy logic systems, the modeling error and the uncertainties are not required to satisfy the constraint conditions. In the meantime, fuzzy T-S models are used to approximate the nonlinear systems and fu...

Constructing concise fuzzy rule bases from databases containing many features present an important yet challenging goal in the current researches of fuzzy rule-based systems. Utilization of all available attributes is not realistic due to the “curse of dimensionality” with respect to the rule number as well as the overwhelming computational costs. This paper proposes a general framework to trea...

This chapter presents a new optimization method for clustering fuzzy data to generate Type-2 fuzzy system models. For this purpose, first, a new distance measure for calculating the (dis)similarity between fuzzy data is proposed. Then, based on the proposed distance measure, Fuzzy c-Mean (FCM) clustering algorithm is modified. Next, Xie-Beni cluster validity index is modified to be able to valu...

Neuro-fuzzy modeling may be qualified as a grey-box technique, since it combines the transparency of rule-based fuzzy systems with the learning capability of neural networks. The main problem in the identification of non-linear processes is the lack of complete information. Certain variables are, either immeasurable or difficult to measure, the soft sensors are the necessary tools to solve the ...