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OPTIMAL UNSUPERVISED MOTOR LEARNING FOR DIMENSIONALITY REDUCTION OF NONLINEAR CONTROL-SYSTEMS

IEEE TRANSACTIONS ON NEURAL NETWORKS 1994年第6期5卷 965-973页

作者： SANGER, TD NASA Jet Propulsion Laboratory Pasadena CA USA

In this paper, optimal unsupervised motor learning is defined to be a technique for finding the coordinate system of minimum dimensionality which can adequately describe a particular motor task. An explicit method is provided for learning a stable controller that translates commands within the new coordinate system into motor variables appropriate for plant control. The method makes use of previously described Neural Network algorithms including the Generalized Hebbian Algorithm [1], Basis-Function Trees [2], and Trajectory Extension Learning [3]. Examples of applications to a real direct-drive two joint planar robot arm and a simulated three joint robot arm with visual sensing are given.

关键词： Nonlinear control systems Control systems backpropagation algorithms Robot sensing systems Robot kinematics Unsupervised learning Sensor systems Bandwidth Neural networks Supervised learning

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Diagnosing failed distribution transformers using neural networks

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IEEE TRANSACTIONS ON POWER DELIVERY 2001年第4期16卷 631-636页

作者： Farag, AS Mohandes, M Al-Shaikh, A King Fahd Univ Petr & Minerals Dhahran 31261 Saudi Arabia SCECO E EDSD EED Dammam Saudi Arabia

An Artificial Neural Networks (ANN) system was developed for distribution transformer's failure diagnosis. The diagnosis was based on the latest standards and expert experiences in this field. The ANN was trained utilizing Back Propagation Algorithm using a real (out of the field) data obtained from utilities distribution networks transformer's failures. The ANN consists of six individual ANN according to six important factors used to give certain outputs. These factors are: the age of the transform, the weather condition, if there are any damaged bushings?, if there are any damaged casing or enclosure?, if there is oil leakage?, and if there are any faults in the windings?. The six ANNs are combined in one ANN to give all the outputs of the individual six ANNs. The developed ANN can be used to give recommended complete diagnosis for working transformers to avoid possible failures depending on their operating conditions. Good diagnosis accuracy is obtained. with the proposed approach applied and with the analysis of the attainable results.

关键词： backpropagation bushings distribution networks failure analysis fault diagnosis neural nets power engineering computing power transformer insulation power transformers transformer oil transformer windings ANN training backpropagation algorithm damaged bu ELECTRIC TRANSFORMERS Transformer windings backpropagation algorithms transformer oil transformer insulation back propagation Power engineering computing Fault diagnosis Power grids Neural network FAILURE ANALYSIS

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The cascade-correlation learning: A projection pursuit learning perspective

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第2期7卷 278-289页

作者： Hwang, JN You, SS Lay, SR Jou, IC MINIST TRANSPORTAT & COMMUN TELECOMMUN LABS CHUNGLI 320 TAIWAN

Cascade-correlation (Cascor) is a popular supervised learning architecture that dynamically grows layers of hidden neurons of fixed nonlinear activations (e.g., sigmoids), so that the network topology (size, depth) can be efficiently determined. Similar to a cascade-correlation learning network (CCLN), a projection pursuit learning network (PPLN) also dynamically grows the hidden neurons. Unlike a CCLN where cascaded connections from the existing hidden units to the new candidate hidden unit are required to establish high-order nonlinearity in approximating the residual error, a PPLN approximates the high-order nonlinearity by using trainable parametric or semiparametric nonlinear smooth activations based on minimum mean squared error criterion. An analysis is provided to show that the maximum correlation training criterion used in a CCLN tends to produce hidden units that saturate and thus makes it more suitable for classification tasks instead of regression tasks as evidenced in the simulation results. It is also observed that this critical weakness in CCLN can also potentially carry over to classification tasks, such as the two-spiral benchmark used in the original CCLN paper.

关键词： Neurons Network topology Laboratories backpropagation algorithms Supervised learning Analytical models Multilayer perceptrons Biological neural networks Neural networks Multi-layer neural network

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WAVELET NETWORKS

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1992年第6期3卷 889-898页

作者： ZHANG, QG BENVENISTE, A INST NATL RECH INFORMAT & AUTOMAT INST RECH INFORMAT & SYST ALEATOIRESF-35042 RENNESFRANCE

Based on the wavelet transform theory, the new notion of wavelet network is proposed as an alternative to feed-forward neural networks for approximating arbitrary nonlinear functions. An algorithm of backpropagation t... 详细信息

关键词： Neural networks Feedforward neural networks backpropagation algorithms Discrete transforms Continuous wavelet transforms Wavelet transforms Nonlinear systems Power system modeling Convergence

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LEARNING IN LINEAR NEURAL NETWORKS - A SURVEY

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1995年第4期6卷 837-858页

作者： BALDI, PF HORNIK, K CALTECH DIV BIOLPASADENACA 91109 VIENNA TECH UNIV INST STAT & WAHRSCHEINLICHKEITSTHEORIEA-1040 VIENNAAUSTRIA

Networks of linear units are the simplest kind of networks, where the basic questions related to learning, generalization, and self-organization can sometimes be answered analytically, We survey most of the known results on linear networks, including: 1) backpropagation learning and the structure of the error function landscape, 2) the temporal evolution of generalization, and 3) unsupervised learning algorithms and their properties, The connections to classical statistical ideas, such as principal component analysis (PCA), are emphasized as well as several simple but challenging open questions, A few new results are also spread across the paper, including an analysis of the effect of noise on backpropagation networks and a unified view of all unsupervised algorithms.

关键词： Intelligent networks Neural networks Unsupervised learning backpropagation algorithms Principal component analysis Neurons Computer networks Evolution (biology) Algorithm design and analysis Biology computing

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Optimization of HV electrode systems by neural networks using a new learning method

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IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION 1996年第6期3卷 737-742页

作者： Mukherjee, PK Trinitis, C Steinbigler, H TECH UNIV MUNICH HIGH VOLTAGE INSTD-8000 MUNICHGERMANY

To avoid a large number of iterations, optimization of electrode shapes has been done by artificial neural networks (NN). Two practical examples have been considered, an axisymmetric single-phase GIS bus termination and an axisymmetric transformer shield ring. The shape of the electrodes has been taken as quarter-ellipse or half-ellipse because an ellipse has more flexibility a than circle. For NN, the socalled resilient propagation algorithm, learning faster than the standard back-propagation algorithm, has been employed. The training sets as well as the test sets of NN have been prepared by charge simulation method.

关键词： Optimization methods Electrodes Neural networks Learning systems Neurons Artificial neural networks Shape Computational modeling backpropagation algorithms Power transformer insulation

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CARVE - A constructive algorithm for real-valued examples

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1998年第6期9卷 1180-1190页

作者： Young, S Downs, T Univ Oxford Dept Expt Psychol Oxford OX1 3UD England Univ Queensland Dept Elect & Comp Engn Brisbane Qld 4072 Australia

A constructive neural-network algorithm is presented. For any consistent classification task on real-valued training vectors, the algorithm constructs a feedforward network with a single hidden layer of threshold units which implements the task, The algorithm, which we call CARVE, extends the "sequential learning" algorithm of Marchand et al. from Boolean inputs to the real-valued input case, and uses convex hull methods for the determination of the network weights. The algorithm is an efficient training scheme for producing near-minimal network solutions for arbitrary classification tasks. The algorithm is applied to a number of benchmark problems including German and Sejnowski's sonar data, the Monks problems and Fisher's iris data. A significant application of the constructive algorithm is in providing an initial network topology and initial weights for other neural-network training schemes and this is demonstrated by application to backpropagation.

关键词： backpropagation algorithms Pattern classification Sonar applications Iris Network topology Polynomials Neural networks Psychology Search methods Pattern recognition

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A METHOD FOR FUZZY RULES EXTRACTION DIRECTLY FROM NUMERICAL DATA AND ITS APPLICATION TO PATTERN-CLASSIFICATION

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 1995年第1期3卷 18-28页

作者： ABE, S LAN, MS ASAHI DIAMOND IND CO LTD CHIYODA KUTOKYO 102JAPAN

In this paper, we discuss a new method for extracting fuzzy rules directly from numerical input-output data for pattern classification. Fuzzy rules with variable fuzzy regions are defined by activation hyperboxes which show the existence region of data for a class and inhibition hyperboxes which inhibit the existence of data for that class. These rules are extracted from numerical data by recursively resolving overlaps between two classes. Then, optimal input variables for the rules are determined using the number of extracted rules as a criterion. The method is compared with neural networks using the Fisher iris data and a license plate recognition system for various examples.

关键词： Data mining Fuzzy systems Neural networks Input variables Pattern classification Fuzzy neural networks Iris Licenses Knowledge acquisition backpropagation algorithms

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An optimal tracking neuro-controller for nonlinear dynamic systems

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IEEE TRANSACTIONS ON NEURAL NETWORKS 1996年第5期7卷 1099-1110页

作者： Park, YM Choi, MS Lee, KY PENN STATE UNIV DEPT ELECT ENGNUNIVERSITY PKPA 16802

Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function, The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC), The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant, The FFNC is designed using a novel inverse mapping concept by using a neuro-identifier, A generalized backpropagation-through-time (GBTT) algorithm is developed to minimize the general quadratic cost function for the FBNC training, The proposed methodology is useful as an off-line control method where the plant is first identified and then a controller is designed for it, A case study for a typical plant with nonlinear dynamics shows good performance of the proposed OTNC.

关键词： Neural networks Cost function Optimal control Error correction backpropagation algorithms Neurofeedback Steady-state Power engineering and energy Distributed control Power generation

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Heat Flux Estimation in Nonlinear Materials Using Kalman Filter-Enhanced Neural Network

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JOURNAL OF THERMOPHYSICS AND HEAT TRANSFER 2009年第1期23卷 41-49页

作者： Chin, H. Hwang, Y. Deng, S. Natl Def Univ Dept Power Vehicle & Syst Engn Chung Cheng Inst Technol Tao Yuan 33509 Taiwan DAYEH Univ Dept Ind Engn & Technol Management Dacun 51591 Changhua Taiwan Naval Shipbldg Dev Ctr Kaohsiung Taiwan

This paper presents an efficient technique for analyzing the surface heat flux of a space shuttle upon reentry using a nonlinear inverse heat conduction technique based upon a Kalman filter-enhanced Bayesian backpropagation neural network. The continuous-time analog Hopfield neural network is used to solve various forward problems to obtain training data for the Kalman filter-enhanced Bayesian backpropagation neural network. The calibrated Kalman filter-enhanced Bayesian backpropagation neural network is then used to inversely compute the boundary conditions from given sets of temperature data obtained using the continuous-time analog Hopfield neural network. The results show that the proposed method can predict the unknown parameters of the current inverse problems with an accuracy of 0.001 %. The performance of the Kalman filter-enhanced Bayesian backpropagation neural network scheme is shown to be better than that of a Bayesian backpropagation neural network or a stand-alone backpropagation scheme calibrated using a Levenberg-Marquardt backpropagation algorithm.

关键词： A: coefficients for quadratic equation W/mK m~2/s B: coefficients for quadratic equation W/mK~2 m~2/s ? K b: measurement point et al Surface heat flow Space shuttle Heat flux Hopfield neural networks backpropagation algorithms materials using Temperature data Unknown parameter Kalman filters Bayesian inference back propagation Conductive heat transfer Neural network Atmospheric entries

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