This paper presents a fuzzy neural network approach to virtual product design. Contemporary design process requires the development of a new computational intelligent methodology that involves intelligent integration of design, analysis and evaluation, simulation and optimization in a virtual environment. In the paper, a soft-computing framework is developed for engineering design based on a hy...

A number of optimization approaches regarding monitoring network design and sampling optimization procedures have been reported in the literature. Cokriging Estimation Variance (CEV) is a useful optimization tool to determine the influence of the spatial configuration of monitoring networks on parameter estimations. It was used in order to derive a reduced configuration of a nitrate concentrati...

Customers often have various requirements and preferences on a product. A product market can be partitioned into several market segments, each of which contains a number of customers with homogeneous preferences. In this paper, a methodology which mainly involves a market survey, fuzzy clustering, quality function deployment (QFD) and fuzzy optimization, is proposed to achieve the optimal targe...

This paper deals with the problem of image reconstruction from incomplete projections. A novel fuzzy vector objective optimization model is developed by integrating the fuzzy set theory and vector objective optimization (multi-objective decision-making). The objective function is expressed as a membership function, and the minimum operator is taken as a fuzzy operator. Furthermore, a novel iter...

Feature selection is the process of removing the irrelevant features from the datasets and fuzzy clustering of microarray data are the most fascinating machine learning techniques in the real world. The main objective of this paper is selecting the independent components of the microarray data using Independent Component Analysis in order to improve the effectiveness and accuracy of the Fuzzy P...

In this communication the formulation of optimization problems using fuzzy dual parameters and variables is introduced to cope with parametric or implementation uncertainties. It is shown that fuzzy dual programming problems generate finite sets of deterministic optimization problems, allowing to assess the range of the solutions and of the resulting performance at an acceptable computational e...

In this study, we propose a space search algorithm (SSA) and then introduce a hybrid optimization of ANFIS-based fuzzy models based on SSA and information granulation (IG). In comparison with conventional evolutionary algorithms (such as PSO), SSA leads not only to better search performance to find global optimization but is also more computationally effective. In the hybrid optimization of ANF...

Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using c...

in the present study, water allocation of latian dam between agricultural products was evaluated by using inexact two-stage optimization model and the final results were compared to a fuzzy interval. data were collected from the regional water and agricultural organization of tehran province for 1991 to 2009 years. the results showed that by using interval fuzzy programming, 63%, 69%, 49% and 3...