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Adaptive fuzzy wavelet network for robust fault detection and diagnosis in non-linear systems

IET CONTROL THEORY AND APPLICATIONS 2014年第15期8卷 1487-1498页

作者： Shahriari-kahkeshi, Maryam Sheikholeslam, Farid Isfahan Univ Technol Dept Elect & Comp Engn Esfahan *** Iran

Fault is an undesired and unexpected event that changes the system behaviour resulting in performance degradation or even instability, so how to detect and diagnose fault become a great deal in engineering community. In this study, an adaptive fuzzy wavelet network-based fault detection and diagnosis (AFWN-FDD) scheme is proposed for non-linear systems subject to unstructured uncertainty. The proposed scheme is composed of a diagnostic estimator and an adaptive fuzzy wavelet network (AFWN). Diagnostic estimator is designed for residual generation and fault detection and AFWN based on multi-resolution analysis of wavelet transform and fuzzy concept is proposed to approximate the model of fault. learning algorithm of the proposed AFWN-FDD scheme is derived in the Lyapunov stability sense. The proposed scheme can simultaneously detect and estimate multiple incipient and abrupt faults in the presence of uncertainty. Stability analysis for the presented fault detection and diagnosis (FDD) scheme is provided. Furthermore, an extension of the proposed scheme for a class of non-linear systems with unmeasured states is presented. The efficiency and performance of the proposed scheme is evaluated through simulations that are performed for two well-known case studies. Comparison results highlight the superiority and capability of the proposed scheme.

关键词： wavelet transforms fuzzy set theory adaptive control robust control fault diagnosis nonlinear control systems Lyapunov methods stability learning systems robust fault detection robust fault diagnosis nonlinear systems performance degradation engineering community adaptive fuzzy wavelet network-based fault detection and diagnosis scheme AFWN-FDD scheme unstructured uncertainty diagnostic estimator multiresolution analysis wavelet transform learning algorithm Lyapunov stability sense stability analysis

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Sliding-window hybrid quasi-Newton algorithm-trained MBER equalisers

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2014年第1期28卷 96-106页

作者： Zhu, Renxiang Wu, Lenan Ningbo Univ Technol Sch Elect & Informat Engn Ningbo 315016 Zhejiang Peoples R China Southeast Univ Sch Informat Sci & Engn Nanjing 210096 Jiangsu Peoples R China

Nonlinear equalisers based on minimum BER are proposed for the equalisation of nonlinear time-varying channels. To train the equalisers online, a sliding-window-based hybrid quasi-Newton algorithm is proposed. Switching between sliding-window stochastic gradient algorithm and sliding-window quasi-Newton algorithm makes the new algorithm significantly stabler with a fast convergence rate. Results from extensive simulation tests show that performance of nonlinear equalisers based on minimum BER is better than the equaliser based on minimum mean square error. The proposed algorithm demonstrates high efficiency as well. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： nonlinear equalisers learning algorithm time-varying channels minimum BER

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Joint Relay Selection and Channel Allocation in Cooperative Communication: A Game Theoretic learning Solution

Joint Relay Selection and Channel Allocation in Cooperative ...

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International Conference on Wireless Communications and Signal Processing

作者： Cheng Ding Liang Shen Dianxiong Liu College of Communications Engineering PLA University of Science and Technology

ISBN: (纸本)9781467376884

This article investigates the issue of joint relay selection and channel allocation in LTE relay networks, where the available channels of each node are restricted and heterogeneous. We decompose the issue into two subproblems: one is the channel selection of relay to mitigate the interference among relay nodes and another is joint relay and channel selection of source node to maximize the total capacity. The first subproblem is formulated as a global interaction game and a learning algorithm is proposed to achieve the Nash equilibrium. Based on the result of the first subproblem, the second one is formulated as a congestion game with player-specific payoff function. We propose a distributed solution to maximize the total capacity. Simulation results show that our approach can obtain a large total capacity and a high fairness index.

关键词： Long Term Evolution channel allocation cooperative communication game theory learning (artificial intelligence) radiofrequency interference relay networks (telecommunication) telecommunication traffic LTE relay networks Nash equilibrium channel selection congestion game game theoretic learning solution global interaction game learning algorithm player-specific payoff function relay nodes relay selection source node Channel allocation Cooperative communication Games Indexes Interference Relays

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Fully fuzzy polynomial regression with fuzzy neural networks

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NEUROCOMPUTING 2014年 142卷 486-493页

作者： Otadi, Mahmood Islamic Azad Univ Dept Math Firoozkooh Branch Firoozkooh Iran

In this paper a polynomial fuzzy regression model with fuzzy independent variables and fuzzy parameters is discussed. Within this paper the fuzzy neural network model is used to obtain an estimate for the fuzzy parameters in a statistical sense. Based on the extension principle, a simple algorithm from the cost function of the fuzzy neural network is proposed, in order to find the approximate parameters. Finally, we illustrate our approach by some numerical examples. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Fuzzy regression Fuzzy neural networks learning algorithm

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Icing load accretion prognosis for power transmission line with modified hidden semi-Markov model

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IET GENERATION TRANSMISSION & DISTRIBUTION 2014年第3期8卷 480-485页

作者： Wu, Xin Li, Lin Rui, Xiaoming North China Elect Power Univ Sch Energy Power & Mech Engn Beijing 102206 Peoples R China

Ice accretion on power transmission lines is one of the major causes for cable failure in Zhaotong area, Yunnan Province, South China. This study proposes a method to predict the remaining-dangerous time (RDT) of the icing load accretion on an interval of the power transmission lines with modified hidden semi-Markov model (HSMM). Based on the predicted RDT of the cables during ice accretion, the appropriate preventative measures can be scheduled in advance by electric power companies. The estimation model with the learning algorithm of support vector machine for icing load accretion is built through historical icing load accretion data and meteorological conditions first. Then, the estimated icing load accretion sequence can be obtained through the estimation model by forecasting the meteorological conditions. The modified HSMM method can eliminate the possible underflow issue during computation, and be used to build the RDT prognosis model. With the estimated icing load accretion sequence and RDT prognosis model, the authors can predict RDT of the icing load accretion on an interval of the power transmission lines. The developed prognosis algorithm is verified through collected meteorological conditions and icing load accretion data on the Dazheng 73# power transmission line in Zhaotong area, Yunnan Province, South China.

关键词： freezing hidden Markov models power cables power engineering computing power transmission lines support vector machines icing load accretion prognosis power transmission line modified hidden semiMarkov model cable failure Zhaotong area Yunnan Province South China remaining-dangerous time electric power companies learning algorithm support vector machine historical icing load accretion data meteorological conditions estimation model RDT 30 prognosis model Zhaotong area Yunnan Province

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Forging a Close Relationship with Multimedia Communities

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IEEE MULTIMEDIA 2014年第4期21卷 14-15页

作者： Zeng, Wenjun Liu, Zicheng Steinbach, Eckehard Univ Missouri Columbia MO 65211 USA Microsoft Res Asia Beijing Peoples R China Microsoft Res Redmond WA USA Tech Univ Munich D-80290 Munich Germany

In May 2014, the authors of the top 26 papers from the IEEE International Conference on Multimedia & Expo (ICME) 2014 were invited to submit extended versions of their papers to this fast track special issue. After a rigorous peer-review process, eight of those submissions were accepted for this special issue, now titled "Hot Topics in Multimedia Research." This is just the beginning of a close collaboration between MM and major multimedia conferences.

关键词： Distributed Video Coding Multimedia Multimedia Research ICME Visual Analysis Object Tracking Image Quality Assessment learning algorithm Location Propagation Gaze Estimation

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A convergent algorithm for a cascade network of multiplexed dual output discrete perceptrons for linearly nonseparable classification

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2014年第2期22卷 380-399页

作者： Genc, Ibrahim Guzelis, Cuneyt Istanbul Medeniyet Univ Fac Engn & Architecture Goztepe Kadikoy Istanbu Turkey Izmir Univ Econ Fac Engn & Comp Sci Izmir Turkey

In this paper a new discrete perceptron model is introduced. The model forms a cascade structure and it is capable of realizing an arbitrary classification task designed by a constructive learning algorithm. The main idea is to copy a discrete perceptron neuron's output to have a complementary dual output for the neuron, and then to select, by using a multiplexer, the true output, which might be 0 or 1 depending on the given input. Hence, the problem of realization of the desired output is transformed into the realization of the selector signal of the multiplexer. In the next step, the selector signal is taken as the desired output signal for the remaining part of the network. The repeated applications of the procedure render the problem into a linearly separable one and eliminate the necessity of using the selector signal in the last step of the algorithm. The proposed modification to the discrete perceptron brings universality with the expense of getting just a slight modification in hardware implementation.

关键词： Discrete perceptron cascade model learning algorithm constructive method

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The Use of Neural Network Predictors for Analyzing the Elevator Vibrations

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2014年第2期39卷 1157-1170页

作者： Taplak, Hamdi Erkaya, Selcuk Yildirim, Sahin Uzmay, Ibrahim Erciyes Univ Vocat Sch Kayseri TR-38039 Kayseri Turkey Erciyes Univ Fac Engn Dept Mechatron Engn TR-38039 Kayseri Turkey

In spite of advanced electro-mechanical technology on passenger or load elevators, elevator accidents still occur. Therefore, it is necessary to analyze vibrations of elevators with and without load for predicting some possible faults on their mechanical parts. This study proposes an adaptive neural network predictor to estimate and evaluate the vibrations on elevator systems. For this purpose, elevator vibrations are measured from two points of the elevator system for different working conditions, and different types of neural network analyzers are employed to evaluate the system vibrations. Simulation results show that neural networks can be used as an adaptive analyzer for such systems in the experimental applications.

关键词： Elevator vibration Neural network learning algorithm

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A Novel learning algorithm Inspired by the concept of DNA Sequence in Biological Systems

A Novel Learning Algorithm Inspired by the concept of DNA Se...

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2010 International Conference on Educational and Network Technology(2010教育与网络技术国际会议 ICENT 2010)

作者： Dongmei Lin Guojie Yu Haiyun Qiao Hebei University of Engineering Handan China

ISBN: (纸本)9781424476602;9781424476619;9781424476626

In this paper, a novel algorithm named DNA-like learning algorithm is proposed. This algorithm is enable to quickly train the CNN template implementing LSBF, and has many advantages including without the need to consider its convergence property, in particular faster running speed and better robustness. The important problem of implementing non-LSBF will be further discussed in the future study.

关键词： Index Terms—DNA Biological Systems learning algorithm

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Toward a Reversed Dictionary of French Sign Language (FSL) on the Web

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14th International Conference on Computers Helping People with Special Needs (ICCHP)

作者： Zbakh, Mohammed Haddad, Zehira Krahe, Jaime Lopez Univ Paris 08 Thim Chart St Denis France

ISBN: (纸本)9783319085999;9783319085982

On the web, we can find dictionaries for viewing a sign of French Sign Language (FSL), from a word. However, finding a word from a sign is much more complicated. For this purpose, we propose to design a web application to find the meaning of a FSL sign in the French language from the sign's features. In order to do this, we have developed an intelligent system capable of learning and self-improving by feeding off the information presented to it during its use. We have managed to find a middle ground between the reliability of the results and the ergonomics of Human-Machine Interfaces (HMI).

关键词： Human Machine Interface Classification algorithms French Sign Language learning algorithm

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