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检索条件"任意字段=Proceedings of the IEE Colloquium on Advances in Neural Networks for Control and Systems"

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Hierarchical competitive net architecture

Hierarchical competitive net architecture

引用

iee colloquium on advances in neural networks for control and systems

作者： T.W. Long E.L. Hanzevack Neuro Dyne Inc. Williamsburg VA USA University of Southern California USA

Hypersonic aircraft require a high degree of system integration. Design tools are needed that can provide rapid, accurate calculations of complex fluid flow. Existing methods are slow. The goal of this project was to apply neural networks to the calculation of fluid flow and heat transfer in a heat exchanger panel for the National AeroSpace Plane (NASP).< >

关键词： Aircraft Design automation neural networks

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proceedings of the 1997 4th International Conference on advances in Power System control, Operation and Management. Part 1 (of 2)

Proceedings of the 1997 4th International Conference on Adva...

引用

proceedings of the 1997 4th International Conference on advances in Power System control, Operation and Management. Part 1 (of 2)

The proceedings contains 73 papers from the Fourth International Conference on advances in Power System control, Operation and Management. Topics discussed include: operation development;power research;intelligent control systems;change management;flexible alternating current transmission systems;power system planning;neural networks;integrated fuzzy logic generator controller;adaptive variable window algorithm;digital distance protection;high impedance fault protection;power frequency model;voltage support devices;power system voltage stability;electric load forecasting;and distributed feeder expansion planning method.

关键词： Electric power systems

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neural networks for control of industrial processes

Neural networks for control of industrial processes

引用

iee colloquium on advances in neural networks for control and systems

作者： B. Schurmann Corporate Research & Development Siemens AG Munchen Germany

In this contribution, neural concepts and methods for control an their use in industrial applications are discussed and illustrated. Even though there exist numerous conventional approaches for solving control tasks, their realization in practice frequently proves to be very difficult. The author pursues several approaches to using neural networks in the context of nonlinear control tasks. In identification, networks are trained to model the dynamics of an unknown nonlinear plant. The model provides the basis for controller design or system diagnosis. In robot control, networks are trained to model the inverse dynamics of the robot. The inverse model is used to linearize the system which is then accessible for the well-established tools of linear control theory. In another context, a network is used as a nonlinear trainable controller.< >

关键词： Identification Industrial control neural networks Nonlinear systems

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Data analysis by means of Kohonen feature maps for load forecast in power systems

Data analysis by means of Kohonen feature maps for load fore...

引用

iee colloquium on advances in neural networks for control and systems

作者： S. Heine I. Neumann Fachbereich Elektrotechnik Technische Hochschule Leipzig Leipzig Germany

Because of the clustering and dimensionality reduction abilities the Kohonen feature map (KFM) can be the preferable tool for deriving knowledge about dependencies of the load consumption in electrical energy systems (EES). This paper describes the application of the KFM for analysing and splitting extensive load databases. The objective is to get separate clusters of load shapes for making short term load forecast (STLF) models with a high accuracy. The building of the forecast models is based on feedforward neural networks (NN).< >

关键词： Feedforward neural networks Load forecasting Self-organizing feature maps

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Comparison of gradient based training algorithms for multilayer perceptrons

Comparison of gradient based training algorithms for multila...

引用

iee colloquium on advances in neural networks for control and systems

作者： G. Irwin G. Lightbody S. McLoone Control Engineering Research Centre Department of Electrical and Electronic Engineering Queen''s University Belfast Belfast UK

The training speed of batch backpropagation using steepest descent, conjugate gradient and quasi-Newton algorithm for a feedforward neural network are compared. Results illustrating the advantages of the Hessian based... 详细信息

关键词： Backpropagation Conjugate gradient methods Feedforward neural networks Hessian matrices Multilayer perceptrons Newton method

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Learning in neural networks and stochastic approximation methods with averaging

Learning in neural networks and stochastic approximation met...

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iee colloquium on advances in neural networks for control and systems

作者： P.S. Shcherbakov S.N. Tikhonov J.D. Mason K. Warwick Institute of Control Sciences RAS Moscow Russia Department of Cybernetics Reading University Reading UK

The problem of adjusting the weights (learning) in multilayer feedforward neural networks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure is to be performed as fast as possible and in a simple computational fashion, the two requirements which are usually not satisfied practically by the methods developed so far. Moreover, the presence of random inaccuracies are usually not taken into account. In view of these three issues, an alternative stochastic approximation approach discussed in the paper, seems to be very promising.< >

关键词： Approximation methods Feedforward neural networks Learning systems

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Evaluation of non-steady loading limits for network components using artificial neural networks

Evaluation of non-steady loading limits for network componen...

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proceedings of the 2nd International Conference on advances in Power System control, Operation & Management

作者： Wolff, G.B. Haubrich, H.J. Seitz, Th. Aachen Germany

Today, artificial neural networks (ANN) are used in various areas of application, in order to reproduce interdependencies between input and output information, similar to the information processing of the human brain. Especially where analytic modelling and calculation is impossible, or in case of unacceptable calculation times, they present a promising way of problem solving. In this project, the evaluation of the thermal capacity of energy cable, with load curves as input data, is discussed by using an ANN-approach. The presented work deals with the calculation of the temperature during normal and faulty operation. The configuration of the ANN is chosen according to this problems and in addition, the input data, especially for training purposes, is analysed. The results underling sufficient exactness for load limit calculations concerning the thermal behaviour of energy cables.

关键词： Electric power systems

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Constructive learning-industrial perspectives

Constructive learning-industrial perspectives

引用

iee colloquium on advances in neural networks for control and systems

作者： R. Murray-Smith K. Hunt F. Lohnert Systems Technology Research Daimler Benz Aerospace Berlin Germany

The learning algorithms and structures which have become popular in the neural network community over the last few years have been successfully applied to various challenging modelling problems. In contrast to the linear modelling methods, there is still no clearly defined engineering process from which a model can reliably be created from measurements taken from a physical system. Problems for the practical application of neural networks involve the interpretation of the trained models, and the explicit introduction of a priori models into the learning system, as well as the use, in many cases, only basic ad hoc validation and experiment design techniques. This talk will discuss several methods which can improve the engineering aspects of model identification with neural nets.< >

关键词： Learning systems neural networks

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Adaptive neurofuzzy systems for difficult modelling and control problems

Adaptive neurofuzzy systems for difficult modelling and cont...

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iee colloquium on advances in neural networks for control and systems

作者： M. Brown C.J. Harris Advanced Systems Research Group Department of Aeronautics & Astronautics University of Southampton Southampton UK

The ability to learn is the cornerstone of current neural technology. It was responsible for their decline in the late sixties when it was shown that the perceptron training algorithm could not be easily extended to multilayered networks, and also the revival of interest in these techniques was initiated by the discovery of a multilayer network adaptation rule. Artificial neural networks attempt to mimic a human's information processing capabilities by building neuronally inspired systems which learn to interact with their environment in a desirable fashion. There are many engineering problems which could benefit from the use such of such systems, although there are also problems with applying these adaptive networks. Most artificial neural networks can be regarded as "black box" learning systems; they are difficult initialise as knowledge is stored in an opaque fashion and validation can only performed using input/output data; their internal structure provides little information to an engineer. In addition, the generalisation, modelling and learning abilities of these networks are generally poorly understood, although such results are necessary when these adaptive systems are applied online in safety critical situations.< >

关键词： Adaptive systems Fuzzy neural networks Learning systems

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Adaptive neurocontrol of MIMO systems based on stability theory

Adaptive neurocontrol of MIMO systems based on stability the...

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iee colloquium on advances in neural networks for control and systems

作者： J.-M. Renders M. Saerens H. Bersini France Laboraioire lridia Université Libre de Bruxelles Brussels Belgium Lab. IRIDIA Univ. Libre de Bruxelles Belgium

In this paper we prove the stability of a certain class of nonlinear discrete MIMO systems controlled by a multilayer neural net with a simple weight adaptation strategy. The proof is based on the Lyapunov formalism. The stability statement is, however, only valid if the initial weight values are not too far from their optimal values that allow perfect model matching. We therefore propose to initialize the weights with values that solve the linear problem. This extends our previous work (Renders, 1993; Saerens, Renders and Bersini, 1993), where SISO systems were considered.< >

关键词： Adaptive control Lyapunov methods Multilayer perceptrons Multivariable systems Neurocontrollers Nonlinear systems Stability

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