Iranian journal of fuzzy systems
Volume:8 Issue: 3, Oct 2011

Abstract. In practice, obtaining the global optimum for the economic dispatch (ED) problem with ramp rate limits and prohibited operating zones is presents difficulties. This paper presents a new and efficient method for solving the economic dispatch problem with non-smooth cost functions using a Fuzzy Adaptive Genetic Algorithm (FAGA). The proposed algorithm deals with the issue of controlling the exploration and exploitation capabilities of a heuristic search algorithm in which the 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 obtained by the proposed FAGA, it is compared with a Real Genetic Algorithm (RGA). Moreover, the results obtained 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 economic load 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 parameters of the constraints are fuzzy, random or fuzzy random variables. To defuzzify the constraints, we consider possibility levels. By chance-constrained programming the chance constraints are converted to crisp constraints which are neither fuzzy nor stochastic and then the resulting classical optimal control problem with crisp constraints is solved by the Pontryagin Minimum Principle and Kuhn-Tucker conditions. The model is illustrated by two numerical examples.

Improving time series forecasting accuracy is an important yet often dicult task. Both theoretical and empirical ndings have indicated that integration of several models is an e ective way to improve predictive performance, especially when the models in combination are quite di erent. In this paper, a model of the hybrid arti cial neural networks and fuzzy model is proposed for time series forecasting, using autoregressive integrated moving average models. In the proposed model, by rst modeling the linear components, autoregressive integrated moving average models are combined with the these hybrid models to yield a more general and accurate forecasting model than the traditional hybrid arti cial neural networks and fuzzy models. Empirical results for nancial time series forecasting indicate that the proposed model exhibits e ectively improved forecasting accuracy and hence is an appropriate forecasting tool for nancial time series forecasting.

A memory control for T-S fuzzy discrete-time systems with sto- chastic input delay is proposed in this paper. Di erent 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 e ectiveness and the merit of the proposed method.

Uncertainty inherent in the nancial 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 de nes the expected payo of trading strategies in a fuzzy nancial market within the framework of credibility theory. In addition, a computable integral form is obtained for expected payo of 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 nite unions of disjoint atoms. We also establish that any continuous real valued function de ned on a compact metric space is totally measurable in the variation of a regular nitely purely atomic multisubmeasure and it is also Gould integrable with respect to regular nitely 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 de ne a fuzzy soft set theory and its related properties. We then de ne fuzzy soft aggregation operator that allows constructing more ecient 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.