Iranian journal of fuzzy systems
Volume:9 Issue: 1, Feb 2012

The bi-objective warehouse problem in a crisp environment is often not effective in dealing with the imprecision or vagueness in the values of the problem parameters. To deal with such situations، several researchers have proposed that the parameters be represented as fuzzy numbers. We describe a new algorithm for fuzzy bi-objective warehouse problem using a ranking function followed by an application of tabu search. The method is illustrated on a numerical example، demonstrating the effectiveness of the tabu search method. Numerical results are compared for both fuzzy and crisp versions of the problem.

The concept of intelligently controlling the search process of gravitational search algorithm (GSA) is introduced to develop a novel data mining technique. The proposed method is called fuzzy GSA miner (FGSA-miner). At first a fuzzy controller is designed for adaptively controlling the gravitational coefficient and the number of effective objects, as two important parameters which play major roles on search process of GSA. Then the improved GSA (namely Fuzzy- GSA) is employed to construct a novel data mining algorithm for classification rule discovery from reference data sets. Extensive experimental results on different benchmarks and a practical pattern recognition problem with nonlinear, overlapping class boundaries and different feature space dimensions are provided to show the powerfulness of the proposed method. The comparative results illustrate that performance of the proposed FGSA–miner considerably outperforms the standard GSA. Also it is shown that the performance of the FGSA-miner is comparable to, sometimes better than those of the CN2 (a traditional data mining method) and similar approach which have been designed based on other swarm intelligence algorithms (ant colony optimization and particle swarm optimization) and evolutionary algorithm (genetic algorithm).

This paper investigates the stability analysis of fuzzy relational dynamic systems. A new approach is introduced and a set of sufficient con- ditions is derived which sustains the unique globally asymptotically stable equilibrium point in a first-order fuzzy relational dynamic system with sum- product fuzzy composition. This approach is also investigated for other types of fuzzy relational composition.

This paper investigates the existence and uniqueness of solutions to first-order nonlinear boundary value problems (BVPs) involving fuzzy differential equations and two-point boundary conditions. Some sufficient conditions are presented that guarantee the existence and uniqueness of solutions under the approach of Hukuhara differentiability.

In this study, a Multi-Objective Genetic Algorithm (MOGA) is utilized to extract interpretable and compact fuzzy rule bases for modeling nonlinear Multi-input Multi-output (MIMO) systems. In the process of non- linear system identification, structure selection, parameter estimation, model performance and model validation are important objectives. Furthermore, se- curing low-level and high-level interpretability requirements of fuzzy models is especially a complicated task in case of modeling nonlinear MIMO nonlinear systems. Due to these multiple and conflicting objectives, MOGA is applied to yield a set of candidates as compact, transparent and valid fuzzy models. Also, MOGA is combined with a powerful search algorithm namely Differen- tial Evolution (DE). In the proposed algorithm, MOGA performs the task of membership function tuning as well as rule base identification simultaneously while DE is utilized only for linear parameter identification. Practical applica- bility of the proposed algorithm is examined by two nonlinear system modeling problems used in the literature. The results obtained show the effectiveness of the proposed method.

In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum variance unbiased (UMVU) and Bayesian estimators and then develop the concept of fuzzy estimators for fuzzy parameters based on fuzzy random variables.

This paper presents a new control structure to reduce torque ripple in switched reluctance motor. Although SRM possesses many advantages in motor structure, it su ers from large torque ripple that causes some problems such as vibration and acoustic noise. In this paper another control loop is added and torque ripple is de ned as an objective function. By using fuzzy sliding mode strategy, the DC link voltage is adjusted to optimize the objective function. Simulation results have demonstrated the proposed control method.

In this work, we de ne fuzzy soft (fs) matrices and their operations which are more functional to make theoretical studies in the fs-set theory. We then de ne products of fs-matrices and study their properties. We nally construct a fs-max-min decision making method which can be successfully applied to the problems that contain uncertainties.

This study is an investigation of fuzzy linear regression model for crisp/fuzzy input and fuzzy output data. A least absolutes deviations approach to construct such a model is developed by introducing and applying a new metric on the space of fuzzy numbers. The proposed approach, which can deal with both symmetric and non-symmetric fuzzy observations, is compared with several existing models by three goodness of t criteria. Three well-known data sets including two small data sets as well as a large data set are employed for such comparisons.

Transport route planning is one of the most important and frequent activities in supply chain management. The design of information systems for route planning in real contexts faces two relevant challenges: the complexity of the planning and the lack of complete and precise information. The purpose of this paper is to find methods for the development of transport route planning in uncertainty decision making contexts. The paper uses an approximation that integrates a specific fuzzy-based methodology from Soft Computing. We present several fuzzy optimization models that address the imprecision and/or exibility of some of its components. These models allow transport route planning problems to be solve with the help of metaheuristics in a concise way. A simple numerical example is shown to illustrate this approach.