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Rough Set Based Fuzzy Neural Network for Pattern Classification

Journal of Beijing Institute of Technology 2003年第4期12卷 428-431页

作者：李侃刘玉树 Department of Computer Science and Engineering

A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.

关键词： fuzzy neural network rough sets the least square algorithm back-propagation algorithm

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Passive Fault Tolerant Control System Using Feed-forward Neural Network for Two-Tank Interacting Conical Level Control System Against Partial Actuator Failures and Disturbances ⁎

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IFAC-PapersOnLine 2019年第14期52卷 141-146页

作者： Himanshukumar R. Patel Vipul A. Shah Dharmsinh Desai University Nadiad-387001 Gujarat India

A novel approach for passive fault-tolerant control (PFTC) system design against actuator faults is proposed. The scheme is based on Feed-forward Neural Network (FFNN) plus conventional PI controller, during the fault occurred into system FFNN will give additional control output to the system according to fault magnitude. The FFNN will trained using back-propagation algorithm. To eliminate the steady-state tracking error, the PI controller is also incorporated. The following fault type and input signals are considered: abrupt, step, sine, and trapezoidal trajectory inputs. The effectiveness and the superiority of the proposed approach are demonstrated using Two-Tank Interacting Conical Level Control System (TTICLCS) example. The simulation performed in MATLAB Simulink platform, also different integral errors like IAE, ISE and IATE are presented to validate the proposed approach.

关键词： Actuator fault back-propagation algorithm conical tank disturbances Feed-forward Neural Network fault-tolerant control PI control

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A neural network system for the prediction of process parameters in pressure die casting

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Journal of Materials Processing Technology 1999年 89-90卷 583-590页

作者： Prasad K.D.V. Yarlagadda Eric Cheng Wei Chiang School of Mechanical Manufacturing and Medical Engineering Queensland University of Technology (QUT) 2 George Street Brisbane Qld 4001. Australia

In this work an artificial intelligent neural network system is developed to generate the process parameters for the pressure die casting process. The scope of this work includes analysing a physical model of the pressure die casting filling stage based on the governing equations of die cavity filling, and the collection of feasible casting data for the training of the network through the use of simulation package MELTFLOW and also from experts in the die casting industry. The multi-layer feed-forward network is trained with data collected directly from the industry using MATLAB application tool box. In this work the neural network is developed using three different training algorithms; namely the error back-propagation algorithm, the momentum and adaptive learning algorithm, and the Levenberg–Mrquardt approximation algorithm. It is found that the Levenberg–Mrquardt approximation algorithm is the preferred method for this application, as it reduces the sum-squared error to a small value. The accuracy of the network developed is tested by comparing the data generated from the network with that from an expert from a local die casting industry. It has been realised that with the use of this system the selection of process parameters becomes much simpler to even a novice user without prior knowledge of die casting process and optimisation techniques.

关键词： Pressure die casting Neural networks Levenberg–Marquardt algorithm back-propagation algorithm Transfer function

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Groundwater Level Predictions Using Artificial Neural Networks

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Tsinghua Science and Technology 2002年第6期7卷 574-579页

作者：毛晓敏尚松浩刘翔 Department of Hydraulic and Hydropower Engineering Tsinghua University Beijing 100084 China Department of Environmental Science and Engineering Tsinghua University Beijing 100084 China

The prediction of groundwater level is important for the use and management of groundwater resources. In this paper, the artificial neural networks (ANN) were used to predict groundwater level in the Dawu Aquifer of Zibo in Eastern China. The first step was an auto-correlation analysis of the groundwater level which showed that the monthly groundwater level was time dependent. An auto-regression type ANN (ARANN) model and a regression-auto-regression type ANN (RARANN) model using back-propagation algorithm were then used to predict the groundwater level. Monthly data from June 1988 to May 1998 was used for the network training and testing. The results show that the RARANN model is more reliable than the ARANN model, especially in the testing period, which indicates that the RARANN model can describe the relationship between the groundwater fluctuation and main factors that currently influence the groundwater level. The results suggest that the model is suitable for predicting groundwater level fluctuations in this area for similar conditions in the future.

关键词： groundwater level prediction artificial neural networks back-propagation algorithm auto-correlation analysis

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Modelling of dimensional changes during sintering

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SCIENCE OF SINTERING 2005年第3期37卷 181-187页

作者： Drndarevic, D Djuricic, M Higher Business Tech Sch Uzice 31000 Serbia Monteneg

An approach to modelling the behaviour of dimensions of PM parts during the sintering process for the prediction of dimensional changes is given. The model is developed on the basis of significant process factors by applying a multilayer neural network architecture with the backpropagation learning algorithm. Results of the simulation in the form of diagrams and tables are presented. The presented model gives better results than the one based on statistical analysis of experimental data, i.e. less total mean approximation errors of the part dimensions for 11.4%. A practical result of the model is the determination of compact dimensions to compensate for dimensional changes during sintering.

关键词： modeling sintering PM parts neural networks back-propagation algorithm

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A New On-Line PID Tuning Method Using Neural Networks

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IFAC Proceedings Volumes 2000年第4期33卷 443-448页

作者： A.B. Rad T.W. Bui Yan Li Y.K. Wong Department of Electrical Engineering The Hong Kong Polytechnic University Hunghom Hong Kong

This paper presents an on-line PID tuning control method, based on the parameters of a first-order plus dead-time (FOPDT) model, which are obtained by using Neural Networks (NN). The outputs of the neural networks are the three parameters of the FOPDT model. By combining this algorithm with a conventional PID controller, an adaptive controller is obtained which requires very little a priori knowledge about the plant under control. The simplicity and feasibility of the scheme for real-time control provide a new approach for implementing neural network applications for a variety of on-line industrial control problems. Simulation results demonstrate the feasibility and adaptive property of the proposed scheme.

关键词： Adaptive Control PID Neural Networks back-propagation algorithm

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A NOVEL HYBRID LOCALIZATION METHOD FOR WIRELESS SENSOR NETWORK

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INTERNATIONAL JOURNAL ON SMART SENSING AND INTELLIGENT SYSTEMS 2016年第3期9卷 1323-1340页

作者： Jun, Wang Fu, Zhang Ren Tiansi Xun, Chen Gang, Liu Henan Univ Sci & Technol Coll Agr Engn Luoyang 471003 Henan Peoples R China China Agr Univ Minist Educ Key Lab Modern Precis Agr Syst Integrat Res Beijing 100083 Peoples R China

Wireless sensor network is a kind of brand-new information acquisition platform, which is realized by the introduction of self-organizing and auto-configuration mechanisms. Node localization technology represents a crucial component of wireless sensor network. In this paper, a localization method based on kernel principal component analysis and particle swarm optimization back propagation algorithm is carefully discussed. First of all, taking KPCA as the front-end system to extract the main components of the localization information, and then regarding the nonlinear principal components extracted from distance vectors as the input samples, and meanwhile taking the coordinates of vertices in addition to the region boundary as the output samples, the PSO-BP neural network is trained to achieve the localization model. Finally the localization of unknown nodes can be estimated. The simulation experiment result showed that the method has high ability of stability and precision, and meets the practical need of localization.

关键词： Wireless sensor network localization kernel principal component analysis particle swarm optimization back-propagation algorithm

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Recognition of a Life Distribution Based on a Neural Network

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International Journal of Plant Engineering and Management 2004年第1期9卷 42-45页

作者： GAOShang DepartmentofElectronicsandInformation EastChinaShipbuildingInstituteJiangsu212003P.R.China

In general, we describe three different methods to select an appropriatedistribution form: histogram, probability plots, and hypothesis test. The life distribution isrecognized by a neural network method. The relationship among life distribution with life data isdescribed through threshold and weight of neural networks. The method is convenient to use. Anexample is presented to validate this method, and the results are satisfactory.

关键词： neural network back-propagation algorithm recognition

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Study on Multiple Resonant Points Control Strategy of Contactless Power Transfer System Using Neural Network

Study on Multiple Resonant Points Control Strategy of Contac...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Sun, Yue Li, Yan Ling Dai, Xin Su, Yu Gang Tang, Chun Sen Chongqing Univ Coll Automat Chongqing 630044 Peoples R China

ISBN: (纸本)9781424467129

This paper presents a power control strategy based on multi-resonant operating points, which is realized by multilayer feedforward neural network applying back-propagation algorithm. The full-bridge contactless power transfer system and magnetizing current of the transmitter on primary side are respectively used as research object and control variable. After batch-learning and training, the converged network determines alternating operating duty cycle of each resonant operating point in one cycle. By controlling magnetizing current, it can fulfill the dynamic regulation of transmission power, and increase the energy transfer efficiency. Simulation results show that in the control strategy, magnetizing current on the primary side can be stabilized at any set value for given range, and system has some disturbance restraint performance, satisfied with contactless power transfer system control demands.

关键词： contactless power transfer multi-resonant operating points back-propagation algorithm multilayer feedforward neural network

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A framework for predicting three-dimensional prostate deformation in real time

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INTERNATIONAL JOURNAL OF MEDICAL ROBOTICS AND COMPUTER ASSISTED SURGERY 2013年第4期9卷 E52-E60页

作者： Jahya, Alex Herink, Mark Misra, Sarthak Univ Twente Inst Biomed Technol & Tech Med MIRA NL-7500 AE Enschede Netherlands

backgroundSurgical simulation systems can be used to estimate soft tissue deformation during pre- and intra-operative planning. Such systems require a model that can accurately predict the deformation in real time. In this study, we present a back-propagation neural network for predicting three-dimensional (3D) deformation of a phantom that incorporates the anatomy of the male pelvic region, i.e. the prostate and surrounding structures that support it. MethodIn the experiments and simulations, a needle guide is used to deform the rectal wall. The neural network predicts the deformation based on the relation between the undeformed and deformed shapes of the phantom. Training data are generated using a validated finite element (FE) model of the prostate and its surrounding structures. The FE model is developed from anatomically accurate magnetic resonance (MR) images. An ultrasound-based acoustic radiation force impulse imaging technique is used to measure in situ the shear wave velocity in soft tissue. The velocity is utilized to calculate the elasticities of the phantom. In the simulation study, the displacement and angle of the needle guide are varied. The neural network then predicts 3D phantom deformation for a given input displacement. ResultsThe results of the simulation study show that the maximum absolute linear and angular errors of the nodal displacement and orientation between neural network and FE predicted deformation are 0.03 mm and 0.01 degrees, respectively. ConclusionsThis study shows that a back-propagation neural network can be used to predict prostate deformation. Further, it is also demonstrated that a combination of ultrasound data, MR images and a neural network can be used as a framework for accurately predicting 3D prostate deformation in real time. Copyright (c) 2013 John Wiley & Sons, Ltd.

关键词： finite element neural network surgical simulation system back-propagation algorithm prostate biopsy real time needle insertion ultrasound

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