Papers by Keyword: Fuzzy Model

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Authors: Yuan Ren, Zhi Dan Zhong, Hong Xiao Liu, Xiao Hui Wang
Abstract: This paper proposes particle swarm optimization (PSO) for identification of the Proton Exchange Membrane Fuel Cells (PEMFC) generation systems fuzzy model. The PEM fuel cell generation system efficiency decreases as its output power increases. Thus, an optimum efficiency should exist and should result in a cost-effective PEM fuel cell generation system. The PEMFC generation system cost and efficiency fuzzy model were build, we use the PSO as an optimization engine to indentify the fuzzy model. The simulation results were presented and the results show that we may minimize the total cost of the generation system by using the PSO.
Authors: Yan Jun Li, Xiao Hui Peng, Yu Qiang Cheng, Jian Jun Wu
Abstract: In this paper, the data of faulty sensors reconstruct algorithm of liquid-propellant rocket engine is developed based on adaptive neuro-fuzzy inference system. First, the input parameters selected for method is according to regularity criterion and the relationships between each parameter; second, adaptive neuro-fuzzy inference system is train by normal test, finally, the fuzzy mode is validated by normal data and the data of faulty sensor is reconstructed. The results indicate that this algorithm can reconstruct the data of faulty sensors accurately and show that the fuzzy model approach has good performance in faulty sensors data reconstruct for LRE.
Authors: Yi Xuan Deng, An Peng Deng, Yun Hai Chai
Abstract: According to the feature of enterprise operational ability index system, the paper uses fuzzy method to evaluate enterprise integrate preponderance, sets up the model of multi-arrangement fuzzy evaluation and gives the specific evaluating method about weight coefficient and approach about model.
Authors: José J. Macías-Hernández, Plamen Angelov, Xiao Wei Zhou
Abstract: Prediction of the properties of the crude oil distillation side streams based on statistical methods and laboratory-based analysis has been around for decades. However, it is difficult to identify, control or compensate the dynamic process behavior and the errors from instrumentation for an online model prediction. The objective of this work is to report an application and a study of a novel technique for real-time modelling, namely eXtended Evolving Fuzzy Takagi-Sugeno models (xTS) for prediction and online monitoring of these properties of the refinery distillation process. The results include the online prediction of Soft Sensors for distillation of Naptha and Gasoil Side Streams. The application predicts the quality of the side stream evolving its fuzzy structure and cluster parameters.
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