Journal of Intelligent & Fuzzy Systems - Volume 24, issue 4

Authors: Cateni, Silvia | Colla, Valentina | Nastasi, Gianluca

Article Type: Research Article

Abstract: The paper presents an application of fuzzy logic to the problem of outliers detection. The overall purpose of the work is to point out anomalous data due different causes through a combination of several traditional methods for outliers detection in multivariate datasets and such combination is achieved through a fuzzy inference system. Moreover, the proposed solutions aims to be automatic and self-adaptive, as some parameters which are required for the combination of the different approaches are automatically evaluated by exploiting the available data, without the need of a-priori assumptions or information on a subset of the available data. The proposed …method therefore belongs to the class of the unsupervised outliers detection methods. In order to demonstrate the effectiveness of the developed method, extensive tests have been performed on both a simple case study and a database coming from a real industrial context, where the data have to be filtered before their exploitation for process control purposes. The achieved numerical results are presented and discussed. Show more

Keywords: Outlier detection, fuzzy inference system

DOI: 10.3233/IFS-2012-0607

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 4, pp. 889-903, 2013

Authors: Mon, Yi-Jen | Lin, Chih-Min | Yeh, Rong-Guan

Article Type: Research Article

Abstract: An intelligent control methodology for a long-term ecological systems is developed in this paper. This intelligent control methodology is called as robust recurrent fuzzy neural network control (RRFNNC). This control methodology is used to deal with multi-biomass ecological system which is an uncertain nonlinear system subject to unpredictable but bounded disturbances. This RRFNNC system is comprised of a recurrent fuzzy neural network (RFNN) controller and a robust controller. The RFNN controller is used to approximate an ideal controller; and the robust controller is designed to compensate for the approximation error between the RFNN controller and the ideal controller. The proposed …RRFNNC system is applied to keep the multi-biomasses of ecological system within a stay small neighborhood of the unique nontrivial optimal equilibrium state of the undisturbed exploited ecosystem. For the simulation results of accumulative yield of harvest, more harvest can be obtained by applying the proposed RRFNNC system when compared with state feedback control. Show more

Keywords: Ecological systems, intelligent control, recurrent fuzzy neural network

DOI: 10.3233/IFS-2012-0626

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 4, pp. 905-913, 2013

Article Type: Other

Citation: Journal of Intelligent & Fuzzy Systems, vol. 24, no. 4, pp. 915-919, 2013