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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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On using extended statistical queries to avoid membership queries

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JOURNAL OF MACHINE learning RESEARCH 2002年第3期2卷 359-395页

作者： Bshouty, NH Feldman, V Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

The Kushilevitz-Mansour (KM) algorithm is an algorithm that finds all the "large" Fourier coefficients of a Boolean function. It is the main tool for learning decision trees and DNF expressions in the PAC model with respect to the uniform distribution. The algorithm requires access to the membership query (MQ) oracle. The access is often unavailable in learning applications and thus the KM algorithm cannot be used. We significantly weaken this requirement by producing an analogue of the KM algorithm that uses extended statistical queries (SQ) (SQs in which the expectation is taken with respect to a distribution given by a learning algorithm). We restrict a set of distributions that a learning algorithm may use for its statistical queries to be a set of product distributions with each bit being 1 with probability rho, 1/2 or 1 - rho for a constant 1/2 > rho > 0 (we denote the resulting model by SQ-D-rho). Our analogue finds all the "large" Fourier coefficients of degree lower than clog n (we call it the Bounded Sieve (BS)). We use BS to learn decision trees and by adapting Freund's boosting technique we give an algorithm that learns DNF in SQ-D-rho. An important property of the model is that its algorithms can be simulated by MQs with persistent noise. With some modifications BS can also be simulated by MQs with product attribute noise (i.e., for a query x oracle changes every bit of x with some constant probability and calculates the value of the target function at the resulting point) and classification noise. This implies learnability of decision trees and weak learnability of DNF with this non-trivial noise. In the second part of this paper we develop a characterization for learnability with these extended statistical queries. We show that our characterization when applied to SQ-Dp is tight in terms of learning parity functions. We extend the result given by Blum et al. by proving that there is a class learnable in the PAC model with random classification noise a

关键词： .denote boosting expectation decision trees learning algorithm uniform distribution PAC model Boolean function DNF KM algorithm large Fourier

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Performance prediction of a ground-coupled heat pump system using artificial neural networks

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EXPERT SYSTEMS WITH APPLICATIONS 2008年第4期35卷 1940-1948页

作者： Esen, Hikmet Inalli, Mustafa Sengur, Abdulkadir Esen, Mehmet Firat Univ Fac Tech Educ Dept Mech Educ TR-23119 Elazig Turkey Firat Univ Fac Engn Dept Mech Engn TR-23279 Elazig Turkey Firat Univ Fac Tech Educ Dept Elect & Comp Sci TR-23119 Elazig Turkey

This paper describes the applicability of artificial neural networks (ANNs) to predict performance of a horizontal ground-coupled heat pump (GCHP) system. Performance forecasting is the precondition for the optimal control and energy saving operation of heat pump systems. ANNs have been used in varied applications and they have been shown to be particularly useful in system modelling and system identification. In order to train the ANN, limited experimental measurements were used as training data and test data. In this study, in input layer, there are air temperature entering condenser unit and air temperature leaving condenser unit, and ground temperatures (I and 2 in);coefficient of performance of system (COPS) is in output layer. The back propagation learning algorithm with three different variants, namely Levenberg-Marguardt (LM), Pola-Ribiere conjugate gradient (CGP), and scaled conjugate gradient (SCG), and tangent sigmoid transfer function were used in the network so that the best approach can find. The most suitable algorithm and neuron number in the hidden layer are found as LM with seven neurons. For this number level, after the training, it is found that Root-mean squared (RMS) value is 1%, and absolute fraction of variance (R-2) value is 99.999% and coefficient of variation in percent (COV) value is 28.62%. It is concluded that, ANNs can be used for prediction of COPS as an accurate method in the systems. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： artificial neural network learning algorithm ground-coupled heat pump horizontal heat exchanger coefficient of performance

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Fuzzy logic for short term load forecasting

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 1996年第4期18卷 215-222页

作者： Ranaweera, DK Hubele, NF Karady, GG Arizona State University Tempe Arizona USA

The results of an investigation of a fuzzy logic model for short term load forecasting are presented. The proposed methodology uses fuzzy rules to incorporate historical weather and load data. These fuzzy rules are obtained from the historical data using a learning-type algorithm. Test results from daily peak and total load forecasts for one year of data from a large scale power system indicate that the fuzzy rule bases can produce results similar in accuracy to more complicated statistical and back-propagation neural network methods. Copyright (C) 1996 Elsevier Science Ltd.

关键词： short term load forecasting fuzzy logic learning algorithm back-propagation network

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Adaptively optimizing stochastic resonance in visual system

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PHYSICS LETTERS A 1998年第1-2期245卷 79-86页

作者： Yang, T Univ Calif Berkeley Elect Res Lab Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Recent psychophysics experiment has showed that the noise strength could affect the perceived image quality. This work gives an adaptive process for achieving the optimal perceived image quality in a simple image perception array, which is a simple model of an image sensor. A reference image from memory is used for constructing a cost function and defining the optimal noise strength where the cost function gets its minimum point. The reference image is a binary image, which is used to define the background and the object. Finally, an adaptive algorithm is proposed for searching the optimal noise strength. Computer experimental results show that if the reference image is a thresholded version of the sub-threshold input image then the output of the sensor array gives an optimal output, in which the background and the object have the biggest contrast. If the reference image is different from a thresholded version of the sub-threshold input image then the output usually gives a sub-optimal contrast between the object and the background. (C) 1998 Elsevier Science B.V.

关键词： stochastic resonance visual perception retina model learning algorithm

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A feedforward neural network based on Legendre polynomial for solving linear Fredholm integro-differential equations

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2023年第7期100卷 1480-1499页

作者： Shao, Xinping Yang, Liupan Guo, Anqi Hangzhou Dianzi Univ Sch Sci Dept Math Hangzhou Peoples R China Hangzhou Dianzi Univ Sch Sci Dept Math Hangzhou 310018 Zhejiang Peoples R China

This paper presents a method for solving linear Fredholm integro-differential equations using a feedforward neural network based on Legendre polynomials. Firstly, the Legendre polynomials are used to approximate the unknown function and the kernel function in the equations. Secondly, The roots of Legendre polynomials are obtained by using Newton iteration method to obtain Gaussian integration points, and the obtained Gaussian integration points are used as the input nodes of the neural network, and the corresponding weight is learned by the gradient descent method to obtain an approximate solution. Finally, through the numerical value Case analysis to verify the effectiveness of the method.

关键词： Fredholm integro-differential equations feedforward neural network Legendre polynomials Gaussian integration points learning algorithm

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A fuzzy neural network based on fuzzy hierarchy error approach

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2000年第6期8卷 808-816页

作者： Wu, A Tam, PKS Hong Kong Polytech Univ Dept Elect & Informat Engn Kowloon Hong Kong Peoples R China

This letter presents a novel fuzzy neural network, which is composed of an antecedent network and a consequent network. The antecedent network matches the premises of the fuzzy rules and the consequent network implements the consequences of the rules. In the network learning and training phase, a concise and effective algorithm based on the fuzzy hierarchy error approach (FHEA) is proposed to update the parameters of the network. This algorithm is simple to implement and it does not require as many Calculations as some other classic neural network learning algorithms, A model reference adaptive control structure incorporating the proposed fuzzy neural network is studied. Simulation results Of a cart-pole balancing system demonstrate the effectiveness of proposed method.

关键词： adaptive control error approach fuzzy controller fuzzy neural network learning algorithm

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A non-singleton type-2 fuzzy neural network with adaptive secondary membership for high dimensional applications

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NEUROCOMPUTING 2019年 338卷 63-71页

作者： Mohammadzadeh, Ardashir Kayacan, Erkan Univ Bonab Dept Elect Engn Bonab Iran MIT Senseable City Lab Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA

This paper develops a non-singleton type-2 fuzzy neural network (NT2FNN) with type-2 3-dimensional membership functions (MFs) and adaptive secondary membership. A new approach based on the squareroot cubature quadrature Kalman filter (SR-CQKF) is proposed for the training the level of the secondary membership and the centers of membership functions. The consequent parameters are learned by using rule-ordered extended Kalman filter (EKF). To show the applicability and effectiveness of proposed NT2FNN in high dimensional problems, four real-world datasets with 4, 7, 13 and 32 input variables are considered. Additionally, the performance of NT2FNN with the proposed learning algorithm is compared with other well-known neural networks and learning algorithms. The simulations demonstrate that the developed method results in high performance in contrast to the other methods. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Non-singleton type-2 fuzzy neural network High dimensional problems learning algorithm Kalman filter

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Optimal Type-3 Fuzzy System for Solving Singular Multi-Pantograph Equations

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IEEE ACCESS 2020年 8卷 225692-225702页

作者： Ma, Chao Mohammadzadeh, Ardashir Turabieh, Hamza Mafarja, Majdi Band, Shahab S. Mosavi, Amir Shenzhen Inst Informat Technol Sch Digital Media Shenzhen 518172 Peoples R China Univ Bonab Dept Elect Engn Fac Engn Bonab *** Iran Taif Univ Dept Informat Technol Coll Comp & Informat Technol At Taif 21944 Saudi Arabia Birzeit Univ Dept Comp Sci Birzeit 72439 Palestine Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Obuda Univ John von Neumann Fac Informat H-1034 Budapest Hungary Norwegian Univ Life Sci Sch Econ & Business N-1430 As Norway J Selye Univ Dept Informat Komarno 94501 Slovakia Oxford Brookes Univ Sch Built Environm Oxford OX3 0BP England

In this study a new machine learning technique is presented to solve singular multi-pantograph differential equations (SMDEs). A new optimized type-3 fuzzy logic system (T3-FLS) by unscented Kalman filter (UKF) is proposed for solution estimation. The convergence and stability of presented algorithm are ensured by the suggested Lyapunov analysis. By two SMDEs the effectiveness and applicability of the suggested method is demonstrated. The statistical analysis show that the suggested method results in accurate and robust performance and the estimated solution is well converged to the exact solution. The proposed algorithm is simple and can be applied on various SMDEs with variable coefficients.

关键词： Artificial neural networks Differential equations Convergence Licenses Machine learning algorithms Information technology Statistical analysis Machine learning artificial intelligence fuzzy systems Lyapunov stability learning algorithm multi-pantograph differential equations

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Heterogeneous network bandwidth management scheme based on the principal-agent game model

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WIRELESS NETWORKS 2017年第1期23卷 279-287页

作者： Kim, Sungwook Sogang Univ Dept Comp Sci Shinsu Dong 1 Seoul 121742 South Korea

In order to provide more comprehensive network services, a concept of integrated heterogeneous network system was introduced. Until now, lots of researchers have focused on how to efficiently integrate different types of wireless and mobile networks. To exploit the heterogeneous network system operation, an important issue is how to properly manage the network bandwidth. In this study, a new bandwidth management scheme has been proposed by employing the principal-agent game model. Among heterogeneous networks, we have analyzed the asymmetric information situation and developed an effective bandwidth allocation algorithm. Under diverse network condition changes, our principal-agent game approach is essential to provide a suitable tradeoff between conflicting requirements. Simulation results demonstrate that the proposed scheme can obtain a better network performance and bandwidth efficiency than other existing schemes.

关键词： Dynamic bandwidth allocation Principal-agent model Game theory Heterogeneous networks learning algorithm

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