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In practice, obtaining the global optimum for the economic dispatch {bf (ED)}problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new andefficient method for solving the economic dispatch problem with non-smooth cost functions using aFuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm deals with the issue ofcontrolling the exploration and exploitation capabilities of a heuristic search algorithm in whichthe real version of Genetic Algorithm (RGA) is equipped with a Fuzzy Logic Controller (FLC)which can efficiently explore and exploit optimum solutions. To validate the results obtainedby the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the resultsobtained by FAGA and RGA are also compared with those obtained by other approaches reported in the literature.It was observed that the FAGA outperforms the other methods in solving the power system economicload dispatch problem in terms of quality, as well as convergence and success rates.

A central aim of educational research in the area of mathematical modeling and applications is to recognize the attainment level of students at defined states of the modeling process. In this paper, we introduce principles of fuzzy sets theory and possibility theory to describe the process of mathematical modeling in the classroom. The main stages of the modeling process are represented as fuzzy sets in a set of linguistic labels indicating the degree of a student's success in each of these stages. We use the total possibilistic uncertainty on the ordered possibility distribution of all student profiles as a measure of the students' modeling capacities and illustrate our results by application to a classroom experiment.

In this paper, a model of an optimal control problem with chance constraints is introduced. The parametersof the constraints are fuzzy, random or fuzzy random variables. Todefuzzify the constraints, we consider possibility levels. Bychance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical optimalcontrol problem with crisp constraints is solved by thePontryagin Minimum Principle and Kuhn-Tucker conditions. The modelis illustrated by two numerical examples.

Improving time series forecastingaccuracy is an important yet often difficult task.Both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. In this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series forecasting, usingautoregressive integrated moving average models. In the proposedmodel, by first modeling the linear components, autoregressive integrated moving average models arecombined with the these hybrid models to yield amore general and accurate forecasting model than thetraditional hybrid artificial neural networks and fuzzy models. Empirical results for financialtime series forecasting indicate that the proposed model exhibitseffectively improved forecasting accuracy and hence is an appropriate forecasting tool for financial timeseries forecasting.

A memory control for T-S fuzzy discrete-time systems with sto- chastic input delay is proposed in this paper. Dierent from the common assumptions on the time delay in the existing literatures, it is assumed in this paper that the delays vary randomly and satisfy some probabilistic dis- tribution. A new state space model of the discrete-time T-S fuzzy system is derived by introducing some stochastic variables satisfying Bernoulli random binary distribution and using state augmentation method, some criterion for the stochastic stability analysis and stabilization controller design are derived for T-S fuzzy systems with stochastic time-varying input delay. Finally, a nu- merical example is given to demonstrate the eectiveness and the merit of the proposed method.

Uncertainty inherent in the financial market was usually consid- ered to be random. However, randomness is only one special type of uncer- tainty and appropriate when describing objective information. For describing subjective information it is preferred to assume that uncertainty is fuzzy. This paper defines the expected payoof trading strategies in a fuzzy financial market within the framework of credibility theory. In addition, a computable integral form is obtained for expected payoof each strategy.

Agility metrics are difficult to define in general, mainly due to the multidimensionality and vagueness of the concept of agility itself. In this paper, a knowledge-based framework is proposed for the measurement and assessment of public sector agility using the A.T.Kearney model. Fuzzy logic provides a useful tool for dealing with decisions in which the phenomena are imprecise and vague. In the paper, we use the absolute agility index together with fuzzy logic to address the ambiguity in agility evaluation in public sector in a case study.

In this paper we study the relationships existing between total measurability in variation and Gould type fuzzy integrability (introduced and studied in [21]), giving a special interest on their behaviour on atoms and on finite unions of disjoint atoms. We also establish that any continuous real valued function defined on a compact metric space is totally measurable in the variation of a regular finitely purely atomic multisubmeasure and it is also Gould integrable with respect to regular finitely purely atomic multisubmeasures.

In this paper, we de ne the concepts of general fuzzy recognizer, language recognized by a general fuzzy recognizer, the accessible and the coac- cessible parts of a general fuzzy recognizer and the reversal of a general fuzzy recognizer. Then we obtain the relationships between them and construct a topology and some hypergroups on a general fuzzy recognizer.

In this work, we define a fuzzy soft set theory and its related properties. We then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. Finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.

This paper is devoted to the concepts of fuzzy upper and fuzzy lower contra-continuous multifunctions and also some characterizations of them are considered.

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