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A Novel Intuitionistic Fuzzy Inhibitor Arc Petri Net With Error Back Propagation algorithm and Application in Fault Diagnosis

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IEEE ACCESS 2019年 7卷 115978-115988页

作者： Tan, Mingyue Li, Jiming Xu, Guangyuan Cheng, Xuezhen Shandong Univ Sci & Technol Dept Elect Engn & Automat Qingdao 266590 Shandong Peoples R China

The setting and adjustment of the weight parameters in the traditional fault diagnosis method depend entirely on personal experience, and the parameter setting lacks regularity. To reduce the fault diagnosis errors caused by human subjective factors and improve the speed and accuracy of power grid fault diagnosis, we propose a method for power grid fault diagnosis using intuitionistic fuzzy inhibitor arc Petri net (IFIAPN) with error back propagation (bp) algorithm. Firstly, according to the network topology analysis and relay protection configuration setting rules, the inhibitor arc (IA) tuple is introduced into the model structure of the intuitionistic fuzzy Petri net to reduce the ambiguity of protection and circuit breaker action. Then, the weight parameters in the model are trained using a bp neural network algorithm to enhance the objectivity of the parameters. Finally, a simulation of an IEEE-39 node system and a real case study using the Hou-zhong line local grid were used to verify the effectiveness of the fault diagnosis method. The results show that the method can effectively deal with the refusal and mis-operation of multiple circuit breakers and improve the diagnostic efficiency under complex data environment.

关键词： Intuitionistic fuzzy set inhibitor arc Petri net bp algorithm grid fault diagnosis

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A Belief Propagation Based State Estimator for Semi-Intrusive Load Monitoring System

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IEEE ACCESS 2022年 10卷 110309-110322页

作者： Duan, Qihong Li, Feng Liu, Junrong Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Northwest Univ Sch Math Xian 710069 Shaanxi Peoples R China

For industrial application of load monitoring techniques, it is important to establish high-performance state estimators of low-cost and low frequency smart meters (SM) and sensors in a power system, which can run under resource-constrained computing units. Because household electronic appliances often tap power from fixed sockets, a finite state table for the corresponding sensors is suitable and convenient. However, SM in the main line may have an enormous state table. In this study, we propose a belief propagation (bp) algorithm to calculate the power consumption of electronic appliances in a semi-intrusive load monitoring (SILM) system whose SM and sensors have state tables with sizes varying largely. The novelty of the proposed method lies in a continuous approximation to a large state table and a switching scheme between discrete and continuous parts of the SILM system. With datasets from numerical simulations and a real-world experimental SILM system in a set of high-density school buildings within a secondary distribution network, the proposed bp algorithm is compared with relevant state-of-the-art algorithms. The results show that the proposed algorithm achieves a percentage of error (8%), which outperforms the percentage achieved by the other methods, a linear state estimation of 99%, a hidden Markov model of 21%, and a full-discrete bp algorithm of 11%. In addition, the complexity of the proposed algorithm is the least of all methods, and the proposed algorithm can run by SoC on concentrators.

关键词： Sensors State estimation Sensor systems Smart meters Load monitoring Home appliances Intelligent sensors Smart meters factor graph bp algorithm reference probability

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Adaptive robust neural controller for robots

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ROBOTICS AND AUTONOMOUS SYSTEMS 2004年第3期46卷 175-184页

作者： Yildirim, S Erciyes Univ Fac Engn Dept Engn Mech Robot Res Lab Kayseri Turkey

This paper presents an investigation on the trajectory control of a robot using a new type of recurrent neural network. A three-layered recurrent neural network is employed to estimate the forward dynamics model of the robot. Standard backpropagation (bp) algorithm is used as a learning algorithm for this network to minimise the difference between the robot actual response and that predicted by the neural network. This algorithm is employed to update the connection weights of the neural network controller with three layers using a gradient function. (C) 2004 Elsevier B.V. All rights reserved.

关键词： neural network robot bp algorithm trajectory

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Super-resolution image reconstruction based on three-step-training neural networks

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Journal of Systems Engineering and Electronics 2010年第6期21卷 934-940页

作者： Fuzhen Zhu Jinzong Li Bing Zhu Dongdong Ma Institute of Image Information Technology and Engineering Harbin Institute of Technology Heilongjiang 150001 R R. China

A new method of super-resolution image reconstruction is proposed, which uses a three-step-training error backpropagation neural network （bpNN） to realize the super-resolution reconstruction （SRR） of satellite image. The method is based on bpNN. First, three groups learning samples with different resolutions are obtained according to image observation model, and then vector mappings are respectively used to those three group learning samples to speed up the convergence of bpNN, at last, three times consecutive training are carried on the bpNN. Training samples used in each step are of higher resolution than those used in the previous steps, so the increasing weights store a great amount of information for SRR, and network performance and generalization ability are improved greatly. Simulation and generalization tests are carried on the well-trained three-step-training NN respectively, and the reconstruction results with higher resolution images verify the effectiveness and validity of this method.

关键词： image reconstruction super-resolution three-steptraining neural network bp algorithm vector mapping.

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Statistical analysis of a two-layer backpropagation algorithm used for modeling nonlinear memoryless channels: The single neuron case

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1997年第3期45卷 747-756页

作者： Bershad, NJ Ibnkahla, M Castanie, F INST NATL POLYTECH TOULOUSE ENSEEIHT GAPSEF-31077 TOULOUSEFRANCE

Neural networks have been used for modeling the nonlinear characteristics of memoryless nonlinear channels using backpropagation (bp) learning with experimental training data. In order to better understand this neural network application, this paper studies the transient and convergence properties of a simplified two-layer neural network that uses the bp algorithm and is trained with zero mean Gaussian data. The paper studies the effects of the neural net structure, weights, initial conditions, and algorithm step size on the mean square error (MSE) of the neural net approximation. The performance analysis is based on the derivation of recursions for the mean weight update that can be used to predict the weights and the MSE over time. Monte Carte simulations display good to excellent agreement between the actual behavior and the predictions of the theoretical model.

关键词： .initial conditions Statistical Analysis Backpropagation recursions bp algorithm TWT Used for Modeling Analysis of Two-Layer Single Neuron two-layer neural network

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A genetic neural fuzzy system and its application in quality prediction in the injection process

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CHEMICAL ENGINEERING COMMUNICATIONS 2004年第3期191卷 335-355页

作者： Li, EG Jia, L Yu, YS E China Univ Sci & Technol Res Inst Automat Shanghai 200237 Peoples R China

A genetic neural fuzzy system (GNFS) is presented and introduced to quality prediction in the injection process. A hybrid-learning algorithm is proposed, which is divided into two stages to train GNFS. During the first learning stage, the genetic algorithm is used to optimize the structure of GNFS and the membership function of each fuzzy term because of its capability of parallel and global search. On the basis of the first optimized training stages, the back-propagation algorithm (bp algorithm) is adopted to update the parameters of the GNFS to improve its predicting precision and reduce the computation time. The process of constructing a quality prediction model for an injection process based on GNFS is described in detail. The predicted weight of the molded part from the model based on GNFS demonstrates that the proposed GNFS has superior performance and good generalization capability in quality prediction in the injection process.

关键词： genetic algorithm bp algorithm neural fuzzy system quality prediction injection process

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Modeling and hierarchical neuro-fuzzy control for flexure-based micropositioning systems

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2001年第4期32卷 411-435页

作者： Lin, LC Sheu, JW Tsay, JH Natl Chunghsing Univ Dept Mech Engn Taichung 402 Taiwan

The micropositioning system using flexural bearing (e.g., for wafer steppers and coarse-fine positioning systems) is a system of infinite degrees of freedom. It is difficult to design a controller for the partial differential equation of the system directly. In this paper, a closed-form dynamics model is first developed using the assumed modes method and the least squares method. Then, a hierarchical neuro-fuzzy controller using backpropagation (bp) training algorithm is proposed for the precision control and active damping of the micropositioning system. Simulation results show that the suggested strategy can actively suppress the flexible vibration and have high positioning performance.

关键词： flexural bearing micropositioning system modeling bp algorithm hierarchical neuro-fuzzy control

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Correlation analysis of aeroengine operation monitoring using deep learning

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SOFT COMPUTING 2021年第1期25卷 551-562页

作者： Xie, Chuan Zhang, Peng Yan, Zhi Air Force Engn Univ Aeronaut Engn Coll Xian 710038 Peoples R China Air Force Engn Univ Grad Coll Xian 710038 Peoples R China

To analyze the reliability of aeroengine more accurately, based on the analysis of operation reliability and complex reliability, the deep learning method is adopted to deal with the nonlinear and time-varying problems between the state parameters and operation reliability of aeroengine, and the condition monitoring method and deep learning of aeroengine are discussed. The results show that, based on the deep learning integrated network, the remaining useful life of aeroengine is predicted, and the key parameters of aeroengine are fitted and predicted by the back propagation (bp) algorithm. The artificial neural network method is used to predict the aeroengine parameters. For the collected aeroengine monitoring parameters, the greedy layer by layer training algorithm is used to mine the relationship between the parameters, extract the evaluation features, and evaluate the performance degradation, which verify the statistical significance and robustness of the conclusions. The proposed algorithm is more accurate and robust than the results of back bp neural network and support vector machine. It can prevent the over-fitting of small samples in aeroengine condition monitoring and further improve its nonlinear processing and generalization ability.

关键词： Artificial neural network bp algorithm Predictive performance Aeroengine Deep learning

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WEIGHTS OPTIMIZATION OF NEURAL NETWORK VIA IMPROVED BCO APPROACH

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PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER 2008年 83卷 185-198页

作者： Zhang, Y. Wu, L. Southeast Univ Sch Informat Sci & Engn Nanjing Peoples R China

Feed forward neural Network (FNN) has been widely applied to many fields because of its ability to closely approximate unknown function to any degree of desired accuracy. Back Propagation (bp) is the most general learning algorithms, but is subject to local optimal convergence and poor performance even on simple problems when forecasting out of samples. Thus, we proposed an improved Bacterial Chemotaxis Optimization (BCO) approach as a possible alternative to the problematic bp algorithm, along with a novel adaptive search strategy to improve the efficiency of the traditional BCO. Taking the classical XOR problem and sinc function approximation as examples, comparisons were implemented. The results demonstrate that our algorithm is obviously superior in convergence rate and precision compared with other training algorithms, such as Genetic algorithm (GA) and Taboo Search (TS).

关键词： .Genetic algorithm widely sinc RBF FUNCTION APPROXIMATION bacterial XOR chemotaxis FNN bp algorithm IMPROVED BCO

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Prediction of triaxial behavior of recycled aggregate concrete using multivariable regression and artificial neural network techniques

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CONSTRUCTION AND BUILDING MATERIALS 2019年 226卷 534-554页

作者： Xu, Jinjun Chen, Yuliang Xie, Tianyu Zhao, Xinyu Xiong, Beibei Chen, Zongping Nanjing Tech Univ Coll Civil Engn Nanjing 211816 Jiangsu Peoples R China Guangxi Univ Sci & Technol Coll Civil Engn Liuzhou 545006 Peoples R China Univ Adelaide Sch Civil Environm & Min Engn Adelaide SA 5005 Australia South China Univ Technol State Key Lab Subtrop Bldg Sci Guangzhou 510640 Guangdong Peoples R China Guangxi Univ Key Lab Disaster Prevent & Struct Safety China Minist Educ Nanning 530004 Peoples R China

A cost-effective design of concrete elements in compliance with sustainability principles requires to accurately predict the behavior of concrete under various loading conditions. Yet to date available approaches for the prediction and evaluation of recycled aggregate concretes (RAC) under triaxial load are very scarce, which may hinder the transition of such an eco-friendly concrete from lab-based technique to widespread applications. As a first attempt to iron out this issue, this study develops some reliable and accurate analytical tools on the basis of an experimental database containing the results of 193 cylindrical peak stress under axisymmetric triaxial load, 55 cubic peak stress under true triaxial load, 76 peak strains and 61 elastic moduli of RAC retrieved from an extensive review of the literature. First, a new empirical design-oriented model is presented refining the generic form of conventional confinement models for natural aggregate concrete while considering the impacts of RCAs. Then Grey Correlation Analysis (GCA) is conducted to look into the sensitivity of the key parameters that affect the triaxial behavior of RAC. The results of the GCA indicate that the triaxial behavior of RAC mainly depends on the effective water-to-cement binder ratio, aggregate-to-cement ratio, lateral stress conditions, exposure temperature, as well as the RCA replacement ratio. Having the input parameters identified by the GCA, three robust data-mining mathematic tools, namely Multiple Nonlinear Regression (MNR), Artificial Neural Network (ANN), and hybrid Genetic algorithm Artificial Neural Network (GA-ANN) are employed to simulate the mechanical performances of RAC under triaxial load. The results demonstrate that the developed MNR equations and the neural network models satisfactorily predict the behavior of RAC under triaxial load: among those models (including the developed design-oriented one), the ANN optimized with GA performs the best. These arguably point to th

关键词： Recycled aggregate concrete (RAC) Triaxial behavior Grey correlation analysis Multiple nonlinear regression Artificial neural networks bp algorithm Genetic algorithm

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