Soft computing

About: Soft computing is a research topic. Over the lifetime, 6710 publications have been published within this topic receiving 118508 citations.

Fuzzy Grey Cognitive Maps in reliability engineering

Abstract: Current industrial equipment has become more complex and huge. In this case, the conventional reliability techniques cannot correctly support functional assessment. This paper integrates an innovative soft computing methodology, Fuzzy Grey Cognitive Map (FGCM), into a traditional reliability analysis for better knowledge. FGCMs are used for evaluating, modelling and aiding decision-making by examining causal relations among relevant domain concepts. The proposed procedure is illustrated with a reliability analysis of a transformer active part. Twenty failure causes in the transformer's active part are identified and assessed. In addition, six failure scenarios are simulated. The results revealed the potential of the combination of FGCM and failure analysis for complex systems. The proposed methodology exposes the potential benefits it could provide in order to assist electric power system decision-makers to supply its customer electrical energy with a high degree of reliability.

Soft Computing Strategies for Optimized Route Selection in Wireless Sensor Network

Abstract: The routing in the wireless sensor networks has significant part in enhancing the functioning of the network as the improper routing methodologies and the routing deteriorates the energy of the sensor networks, affecting the lifespan of the network, leading to link failures and the connectivity problems. The necessity to improve the network performance is the main focus of the paper, so the paper presents the performance analysis of the evolutionary algorithms such as Genetic Algorithm, Ant lion optimization, Particle swarm optimization and Ant colony optimization and evaluates the routes obtained using the fuzzy Petri Net model, to find out the optimal route for the wireless sensor networks. The simulation through the python in terms of the some resource validates the optimal path in terms of energy, network life time and the packet delivery ratio.

Computational Intelligence in Data Mining

Abstract: Janos Abonyi and Balazs FeilUniversity of Veszprem, Department of Process Engineering,P.O. Box 158, H-8201 Veszprem, Hungary, abonyij@fmt.vein.huwww.fmt.vein.hu/softcompAjith AbrahamSchool of Computer Science and Engineering,Chung-Ang University, Seoul, S. Korea, ajith.abraham@ieee.orghttp://ajith.softcomputing.netKeywords: KDD, Computational Intelligence, Soft Computing, Fuzzy Classifier System,Rule Base Reduction, VisualizationReceived: December 20, 2004This paper is aimed to give a comprehensive view about the links between computational intelligence anddata mining. Further, a case study is also given in which the extracted knowledge is represented by fuzzyrule-based expert systems obtained by soft computing based data mining algorithms. It is recognized thatboth model performance and interpretability are of major importance, and effort is required to keep theresulting rule bases small and comprehensible. Therefore, CI technique based data mining algorithms havebeen developed for feature selection, feature extraction, model optimization and model reduction (rule basesimplification). Application of these techniques is illustrated using the Wine data classification problem.The results illustrate that that CI based tools can be applied in a synergistic manner though the nine stepsof knowledge discovery.Povzetek:

On Soft Metric Spaces

Abstract: After the famous article of Moldotsove [10] in 1999 which initiate the theory of soft sets as a mathematical theory to deal with the uncertainty problems, many research works in the softbmathematics and its applications in various fields are appeared. In [17], the authers introduced a new definition of the soft metric function using the soft elements. By this definition each soft metric in view of Das and Samanta [6] is also a soft metric in our concept but the converse is not true. In the present paper, some soft topological properties are given in details, namely (soft compactness, soft sequentially compactness, continuity and uniformly continues of soft functions between soft topological spaces). We hope that the findings in thispaper will help researcher enhance and promote the further study on soft topology to carry out a general framework for theirapplications in practical life.