Soft computing

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

Time series analysis using soft computing methods

Abstract: The aim of this study is to show that the integration of two soft computing techniques, namely the F-transform and fuzzy tendency modeling, can be successfully used in the analysis and forecasting of time series. The proposed method is based on the two-term additive decomposition of a time series, in which the first term is a low-frequency trend (expressed using direct F-transform components), and the second term is a residual vector that is processed as a stationary time series. A theoretical justification is given, and experiments are included. A practical application that shows the analysis of a time series with economic indicators is demonstrated.

Hybrid Intelligent Systems Design - A Review of a Decade of Research

Abstract: The emerging need for Hybrid Intelligent Systems (HIS) is currently motivating important research and development work The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has in recent years contributed to a large number of new intelligent system designs Soft Computing (SC) introduced by Lotfi Zadeh [1] is an innovative approach to construct computationally intelligent hybrid systems consisting of Artificial Neural Network (ANN), Fuzzy Logic (FL), approximate reasoning and derivative free optimization methods such as Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS) Most of these approaches, however, follow an ad hoc design methodology, further justified by success in certain application domains Due to the lack of a common framework it remains often difficult to compare the various hybrid systems conceptually and evaluate their performance comparatively It has been over a decade since HIS were first applied to solve complicated problems In this paper, we first aim at classifying state--of--the--art intelligent systems, which have evolved over the past decade in the HIS community Some theoretical concepts of ANN, FL and Global Optimization Algorithms (GOA) namely GA, SA and TS are also presented We further attempt to summarize the work that has been done and present the current standing of our vision on HIS and future research directions

Fuzzy reinforcement learning based intelligent classifier for power transformer faults.

Abstract: In this work a fuzzy reinforcement learning (RL) based intelligent classifier for power transformer incipient faults is proposed. Fault classifiers proposed till date have low identification accuracy and do not identify all types of transformer faults. Herein, an attempt has been made to design an adaptive, intelligent transformer fault classifier that progressively learns to identify faults on-line with high accuracy for all fault types. In the proposed approach, dissolved gas analysis (DGA) data of oil samples collected from real power transformers (and from credible sources) has been used, which serves as input to a fuzzy RL based classifier. Typically, classification accuracy is heavily dependent on the number of input variables chosen. This has been resolved by using the J48 algorithm to select 8 most appropriate input variables from the 24 variables obtained using DGA. Proposed fuzzy RL approach achieves a fault identification accuracy of 99.7%, which is significantly higher than other contemporary soft computing based identifiers. Experimental results and comparison with other state-of-the-art approaches, highlights superiority and efficacy of the proposed fuzzy RL technique for transformer fault classification.

Hybrid model for medical diagnosis using Neutrosophic Cognitive Maps with Genetic Algorithms

Abstract: This paper presents a combination of two soft computing techniques: Neutrosophic cognitive maps and Genetic algorithm for modeling of medical disease diagnosis. Earlier, medical decision support system was proposed by many researchers where cognitive maps along with genetic algorithm were experimented. The hybrid model of fuzzy cognitive maps with genetic algorithm was implemented to handle situations where decisions are not clearly distinct. In real world situation data is not always consistent so, authors proposed a new hybrid model of Neutrosophic Cognitive Maps with Genetic Algorithms to handle indeterminacy. The proposed model will provide distinct diagnosis of disease in medical decision support system.

Soft computing for multicustomer due-date bargaining

Abstract: The due-date bargainer is a useful tool to support negotiation on due dates between a manufacturer and its customers. To improve the computational performance of an earlier version of the due-date bargainer, we present a new soft computing approach. It uses a genetic algorithm to find the best priority sequence of customer orders for resource allocation, and fuzzy logic operations to allocate the resources and determine the order completion times, following the priority sequence of orders. To extend the due-date bargainer to accommodate bargaining with several customers at the same time, we propose a method to distribute the total penalty using marginal penalties for the individual bargainers. A demonstration software package implementing the improved due-date bargainer has been developed. It is targeted at apparel manufacturing enterprises. Experiments using realistic resource data and randomly generated orders have achieved satisfactory results.