Topic
Soft computing
About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.
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TL;DR: An expert system approach for image classification according to expert knowledge about best sites for vegetation classes is described, and a possibility theory-based pooling aggregation rule is presented.
33 citations
TL;DR: A new approach is presented that minimizes copper & iron losses and optimizes the efficiency of a variable speed Induction motor drive using Fuzzy model identification and PSO algorithm for loss minimization.
Abstract: This paper presents a new approach that minimizes copper & iron losses and optimizes the efficiency of a variable speed Induction motor drive. This method is based on a simple induction motor field oriented control model includes iron losses uses only conventional IM parameters. In literature, Fuzzy logic and Genetic Algorithms have been used for efficiency optimization of induction motor drives. This paper proposes integration of Fuzzy model identification and PSO algorithm for loss minimization. An improvement of efficiency is obtained by adjusting the magnetizing current component with respect to the torque current component to give the minimum total copper and iron losses. The whole circuit is simulated using MATLAB 7.6. The proposed method is compared with other soft computing techniques. The results obtained by Fuzzy PSO shows better results compared with other approaches. General Terms Algorithms, Performance, Verification
33 citations
TL;DR: A comprehensive review of approaches for dealing with RSD problems over the years in terms of modelling and optimisation of both quantitative and qualitative aspects of the process is presented in this paper.
Abstract: Rapid product development and efficient use of existing resources are key competitive drivers in the steel industry and it is imperative that solution strategies are capable of delivering high quality solutions at low cost. However, traditional search techniques for Rolling System Design (RSD) are ad hoc and users of them find it very difficult in satisfying the required commercial imperatives. This paper presents a comprehensive review of approaches for dealing with RSD problems over the years in terms of modelling and optimisation of both quantitative and qualitative aspects of the process. It critically analyses how such strategies contribute to developing timely low cost optimal solutions for the steel industry. The paper also explores the soft computing based technique as an emerging technology for a more structured RSD optimisation. The study has identified challenges posed by RSD for an algorithmic optimisation approach, especially for evolutionary computing based techniques.
33 citations
TL;DR: An approach that integrates soft-computing techniques in order to facilitate the computer-aided collaboration among designers and produces a simplified problem formulation suitable for addressing redesign tasks in significantly less computational time is presented.
33 citations
TL;DR: The method of F GCMs and a proposed Hebbian-based learning algorithm for FGCMs were applied to a known reference chemical process problem, concerning a control process in chemical industry with two tanks, three valves, one heating element and two thermometers for each tank.
Abstract: Fuzzy Grey Cognitive Maps (FGCM) is an innovative Grey System theory-based FCM extension. Grey systems have become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. In this study, the method of FGCMs and a proposed Hebbian-based learning algorithm for FGCMs were applied to a known reference chemical process problem, concerning a control process in chemical industry with two tanks, three valves, one heating element and two thermometers for each tank. The proposed mathematical formulation of FGCMs and the implementation of the NHL algorithm were analyzed and then successfully applied keeping the main constraints of the problem. A number of numerical experiments were conducted to validate the approach and verify the effectiveness. Also, the produced results were analyzed and compared with the results previously reported in the literature from the implementation of the FCMs and Nonlinear Hebbian learning algorithm. The advantages of FGCMs over conventional FCMs are their capabilities (i) to produce a length and greyness estimation at the outputs; the output greyness can be considered as an additional indicator of the quality of a decision, and (ii) to succeed desired behavior for the process system for every set of initial states, with and without Hebbian learning.
33 citations