Current Issue : January - March Volume : 2014 Issue Number : 1 Articles : 5 Articles
This study develops the water resources management model for conjunctive use of surface and subsurface water using a fuzzy\r\ninference system (FIS). The study applies the FIS to allocate the demands of surface and subsurface water. Subsequently, water\r\nallocations in the surface water system are simulated by using linear programming techniques, and the responses of subsurface\r\nwater system with respect to pumping are forecasted by using artificial neural networks. The operating rule for the water systems is\r\nthat themore abundant water system supplies more water. By using the fuzzy rule, the FIS conjunctive usemodel easily incorporates\r\nexpert knowledge and operational polices into water resources management.Theresult indicates that the FISmodel ismore effective\r\nand efficient when compared with the decoupled conjunctive use and simulation-optimizationmodels. Furthermore, the FIS model\r\nis an alternative way to obtain the conjunctive use policies between surface and subsurface water....
We use a new method based on discrete fuzzy transforms for coding/decoding frames of color videos in which we determine\r\ndynamically the GOP sequences. Frames can be differentiated into intraframes, predictive frames, and bidirectional frames, and we\r\nconsider particular frames, called?-frames (resp.,R-frames), for coding P-frames (resp., B-frames) by using two similaritymeasures\r\nbased on Lukasiewicz ??-norm; moreover, a preprocessing phase is proposed to determine similarity thresholds for classifying the\r\nabove types of frame. The proposed method provides acceptable results in terms of quality of the reconstructed videos to a certain\r\nextent if compared with classical-based F-transforms method and the standard MPEG-4....
In the context of sustainable transportation systems, previous studies have either focused only on the transportation system or have\r\nnot used a methodology that enables the treatment of incomplete, vague, and qualitative information associated with the available\r\ndata.This study proposes a system of systems (SOS) and a fuzzy logic modeling approach. The SOS includes the Transportation,\r\nActivity, and Environment systems. The fuzzy logic modeling approach enables the treatment of the vagueness associated with\r\nsome of the relevant data. Performance Indices (PIs) are computed for each system using a number of performance measures. The\r\nPIs illustrate the aggregated performance of each system as well as the interactions among them. The proposed methodology also\r\nenables the estimation of a Composite Sustainability Index to summarize the aggregated performance of the overall SOS. Existing\r\ndata was used to analyze sustainability in the entire United States. The results showed that the Transportation and Activity systems\r\nfollow a positive trend, with similar periods of growth and contractions; in contrast, the environmental system follows a reverse\r\npattern. The results are intuitive and are associated with a series of historic events, such as depressions in the economy as well as\r\npolicy changes and regulations...
The main objective of this paper is to prove the great advantage that brings our novel approach to the intelligent control area. A set\r\nof various types of intelligent controllers have been designed to control the temperature of a room in a real-time control process in\r\norder to compare the obtained results with each other.Through a training board that allows us to control the temperature, all the\r\nused algorithms should present their best performances in this control process; therefore, our self-organized and online adaptive\r\nfuzzy logic controller (FLC) will be required to present great improvements in the control task and a real high control performance.\r\nSimulation results can show clearly that the new approach presented and tested in this work is very efficient.Thus, our adaptive and\r\nself-organizing FLC presents the best accuracy compared with the remaining used controllers, and, besides that, it can guarantee\r\nan important reduction of the power consumption during the control process....
Neural networks (NNs), type-1 fuzzy logic systems (T1FLSs), and interval type-2 fuzzy logic systems (IT2FLSs) have been shown to\r\nbe universal approximators,whichmeans that they can approximate any nonlinear continuous function. Recent research shows that\r\nembedding an IT2FLS on an NN can be very effective for a wide number of nonlinear complex systems, especially when handling\r\nimperfect or incomplete information. In this paper we show, based on the Stone-Weierstrass theorem, that an interval type-2 fuzzy\r\nneural network (IT2FNN) is a universal approximator,which uses a set of rules and interval type-2membership functions (IT2MFs)\r\nfor this purpose. Simulation results of nonlinear function identification using the IT2FNN for one and three variables and for the\r\nMackey-Glass chaotic time series prediction are presented to illustrate the concept of universal approximation....
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