Soft computing

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

Genetic algorithm with fuzzy fitness function for feature selection

Abstract: Feature selection is an important preprocessing task for any pattern recognition or data mining application. Though lots of well developed statistical and mathematical techniques of feature selection exist they do not match the imprecise and incomplete nature of most of the real world problems. Recently soft computing techniques i.e. neurocomputing, fuzzy logic, genetic algorithm etc. are gaining growing popularity for their remarkable ability of handling real life data like a human being in an environment of uncertainty, imprecision and implicit knowledge. In this work, a genetic algorithm in conjunction with a fuzzy fitness function, a fuzzy measure for evaluation of the quality of a feature has been proposed for feature subset selection. GA based feature selection algorithms are robust but their computation time is high specially when they are used with a classifier for fitness evaluation. The computationally light fuzzy fitness function lowers the computation time of the traditional GA based algorithm with classifier accuracy as the fitness function by separating the two stages feature selection and classification. Simulation over two data sets shows the efficiency of the proposed technique for achieving near optimal solution in practical problems specially when the data set contains a large number of features.

Rainfall prediction model using soft computing technique

Abstract: Rainfall prediction in this paper is a spatial interpolation problem that makes use of the daily rainfall information to predict volume of rainfall at unknown locations within area covered by existing observations. This paper proposed the use of self-organising map (SOM), backpropagation neural networks (BPNN) and fuzzy rule systems to perform rainfall spatial interpolation based on local method. The SOM is first used to separate the whole data space into some local surface automatically without any knowledge from the analyst. In each sub-surface, the complexity of the whole data space is reduced to something more homogeneous. After classification, BPNNs are then use to learn the generalization characteristics from the data within each cluster. Fuzzy rules for each cluster are then extracted. The fuzzy rule base is then used for rainfall prediction. This method is used to compare with an established method, which uses radial basis function networks and orographic effect. Results show that this method could provide similar results from the established method. However, this method has the advantage of allowing analyst to understand and interact with the model using fuzzy rules.

A brief review of modeling approaches based on fuzzy time series

Abstract: Recently, there seems to be increased interest in time series forecasting using soft computing (SC) techniques, such as fuzzy sets, artificial neural networks (ANNs), rough set (RS) and evolutionary computing (EC). Among them, fuzzy set is widely used technique in this domain, which is referred to as “Fuzzy Time Series (FTS)”. In this survey, extensive information and knowledge are provided for the FTS concepts and their applications in time series forecasting. This article reviews and summarizes previous research works in the FTS modeling approach from the period 1993–2013 (June). Here, we also provide a brief introduction to SC techniques, because in many cases problems can be solved most effectively by integrating these techniques into different phases of the FTS modeling approach. Hence, several techniques that are hybridized with the FTS modeling approach are discussed briefly. We also identified various domains specific problems and research trends, and try to categorize them. The article ends with the implication for future works. This review may serve as a stepping stone for the amateurs and advanced researchers in this domain.

Benchmark problems for nonlinear system identification and control using Soft Computing methods

Abstract: HighlightsCollection of 13 benchmark problems described in detail in standardized way.General assessment criteria as well as problem-specific tests specified.Benchmarks span from simple artificial systems to complex entire industrial plants.Many domains covered incl. drives, mechatronics, chemical plants, wind turbines.Examples of use in Soft Computing community are provided for each problem. Using benchmark problems to demonstrate and compare novel methods to the work of others could be more widely adopted by the Soft Computing community. This article contains a collection of several benchmark problems in nonlinear control and system identification, which are presented in a standardized format. Each problem is augmented by examples where it has been adopted for comparison. The selected examples range from component to plant level problems and originate mainly from the areas of mechatronics/drives and process systems. The authors hope that this overview contributes to a better adoption of benchmarking in method development, test and demonstration.