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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25 Jul 2001
TL;DR: The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.
Abstract: Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The article focuses on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.
214 citations
01 Jan 2005
212 citations
TL;DR: In this paper, a neural networks-based damage detection method using the modal properties is presented, which can effectively consider the modelling errors in the baseline finite element model from which the training patterns are to be generated.
210 citations
TL;DR: A missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases, i.e. clustering technique, is presented and it is shown that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.
Abstract: In this paper, we present a missing data imputation method based on one of the most popular techniques in Knowledge Discovery in Databases (KDD), i.e. clustering technique. We combine the clustering method with soft computing, which tends to be more tolerant of imprecision and uncertainty, and apply a fuzzy clustering algorithm to deal with incomplete data. Our experiments show that the fuzzy imputation algorithm presents better performance than the basic clustering algorithm.
206 citations
TL;DR: The architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inferenceSystem implemented in the framework of adaptive networks.
Abstract: paper, we presented the architecture and basic learning process underlying ANFIS (adaptive-network-based fuzzy inference system) which is a fuzzy inference system implemented in the framework of adaptive networks. Soft computing approaches including artificial neural networks and fuzzy inference have been used widely to model expert behavior. Using given input/output data values, the proposed ANFIS can construct mapping based on both human knowledge (in the form of fuzzy if-then rules) and hybrid learning algorithm. In modeling and simulation, the ANFIS strategy is employed to model nonlinear functions, to control one of the most important parameters of the induction machine and predict a chaotic time series, all yielding more effective, faster response or settling times.
206 citations