0957-4174/$ see front matter 2010 Elsevier Ltd. A doi:10.1016/j.eswa.2010.07.112 ⇑ Corresponding author. E-mail addresses: [email protected] (H. I org (A. Abraham). Fuzzy clustering is an important problem which is the subject of active research in several real-world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient,...

This study presents a new approach based on Tanaka's fuzzy linear regression (FLP) algorithm to solve well-known power system economic load dispatch problem (ELD). Tanaka's fuzzy linear regression (FLP) formulation will be employed to compute the optimal solution of optimization problem after linearization. The unknowns are expressed as fuzzy numbers with a triangular membership function that h...

A clonal selection algorithm (CLONALG) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. In this study, a new method is proposed for optimization of the Multiple Input Single Output (MISO) fuzzy membership functions using CLONALG. The most appropriate placement of membership functions with respect to fuzzy variab...

Hybrid fuzzy expert systems are one of the most practical intelligent paradigm of soft computing techniques with the high potential for managing uncertainty associated to the medical diagnosis. The potential of genetic algorithm (GA) by inspiring from natural evolution as a learning and optimization technique has been vastly concentrated for improving fuzzy expert systems. In this paper, the GA...

The objective of this paper is to deal with the fuzzy conic program- ming problems. The aim here is to derive weak and strong duality theorems for a general fuzzy conic programming. Toward this end, The convexity-like concept of fuzzy mappings is introduced and then a speci c ordering cone is established based on the parameterized representation of fuzzy numbers. Un- der this setting, duality t...

Pareto-optimality conditions are crucial when dealing with classic multi-objective optimization problems because we need to find out a set of optimal solutions rather than only one optimal solution to optimization problem with a single objective. Extensions of these conditions to the fuzzy domain have been discussed and addressed in recent literature. This work presents a novel approach based o...

A fuzzy simulation-based optimization approach FSOA is developed for identifying optimal design of a benzene-contaminated groundwater remediation system under uncertainty. FSOA integrates remediation processes i.e., biodegradation and pump-and-treat , fuzzy simulation, and fuzzy-mean-value-based optimization technique into a general management framework. This approach offers the advantages of 1...

This paper proposes a TSK-type Neuro-Fuzzy system tuned with a novel learning algorithm. The proposed algorithm used an improved version of the standard Particle Swarm Optimization algorithm, it employs several sub-swarms to explore the search space more efficiently. Each particle in a sub-swarm correct her position based on the best other positions, and the useful information is exchanged amon...

This article describes an approach to automatically build a Takagi-Sugeno fuzzy model (TSK-model) based on a set of input-output data (system identification). Identifying rule-based fuzzy models consists of two parts: structure modeling, i. e. determining the number of rules and input variables involved respectively, and parameter optimization, i. e. optimizing the rules consequences and the lo...

Several adaptation techniques have been investigated to optimize fuzzy inference systems. Neural network learning algorithms have been used to determine the parameters of fuzzy inference system. Such models are often called as integrated neuro-fuzzy models. In an integrated neuro-fuzzy model there is no guarantee that the neural network learning algorithm converges and the tuning of fuzzy infer...