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

共 141 条记录，以下是121-130 订阅

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MATLAB-neural networks toolbox hardware post-processor

MATLAB-neural networks toolbox hardware post-processor

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iee colloquium on Applied control Techniques Using MATLAB

作者： F. Margrave N.R. Babu A. Bradshaw I. Collins Engineering Department Lancaster University UK

The development of intelligent systems has created a need for reliable development techniques and algorithms for artificial neural networks (ANN) and fuzzy control techniques. The neural Network Toolbox (now at v2.00) provides the first steps toward development of solutions that implement this technology. The output from the software is presented in easily understood graphical and numerical formats and the interactive MATLAB environment enables more creative thinking to be applied to problems. The developer is however faced with the problem of transferring work from MATLAB onto suitable hardware where further research and practical implementation may be conducted. This work seeks to use techniques which other aspects of engineering have used successfully to transfer information onto hardware for practical use. Current work at Lancaster involved with development of ANN architectures and control solutions also involves the use of hardware from neural technologies, in particular the nt6000 system. Network design using standard back propagation designs is also carried out using MATLAB and the NN toolbox. The work discussed in this presentation shows how techniques were developed to allow the direct transfer of code from MATLAB to this hardware.

关键词： Backpropagation Design automation Fuzzy control Fuzzy neural networks Graphical user interfaces Multilayer perceptrons neural network hardware neural network architecture

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RAM-based neural networks for image reconstruction in process tomography

RAM-based neural networks for image reconstruction in proces...

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iee colloquium on Innovations in Instrumentation for Electrical Tomography

作者： P.M. Duggan T.A. York Department of Electrical Engineering and Electronics UMIST (University of Manchester Institute of Science and Technology) UK

The paper describes preliminary investigations into the application of RAM-based neural networks to image reconstruction for tomographic systems. Amenability to hardware implementation and the trivial mathematics involved in recall suggest that the RAM-based approach may allow for high speed reconstruction of images at a fraction of the cost of traditional reconstruction methods. Simulated data for a 12 electrode capacitance tomography system, with a 2-phase flow regime, have been generated using finite element modelling. Through extensive software simulations, image flows have been reconstructed from capacitance measurements. Results for two flow regimes (stratified and bubble) are presented. Careful selection of the training patterns and network parameters reveals that high fidelity images can be reconstructed.

关键词： Tomography Image reconstruction neural networks Random access memories Capacitance measurement Learning systems Finite element methods Bubbles Inverse problems Monitoring Process control

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Structural problems in adaptive neuro control

Structural problems in adaptive neuro control

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iee colloquium on Adaptive control (Digest No: 1996/139)

作者： M. French E. Rogers ISIS Research Group Department of Electronics and Computer Science University of Southampton

We study the relation between neural network based control and nonlinear parametric adaptive control. We consider the static problem and give results which show that, for smooth systems, continuous stabilising controls imply the existence of neural network controls for compact sets of initial conditions. We discuss the possibilities for global results and show that this is possible in a very restrictive sense. controllability and observability are briefly considered. We illustrate a control problem (speed field tracking) which cannot even be approximately solved by neural networks. These static results serve to illustrate the fundamental limitations of approximate parameterisations which will impose limitations on the adaptive schema. Finally, we illustrate the adaptive situation by a feedback linearisation scheme.

关键词： Adaptive control

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Parallel processing on the simulation of flexible structures

Parallel processing on the simulation of flexible structures

引用

iee colloquium on Recent advances in Parallel Processing for control

作者： P.K. Kourmoulis G.S. Virk Department of Control Engineering University of Sheffield UK

The problem of controlling flexible structures is important but challenging due to the infinite number of modes present. For example to suppress vibrations of aircraft fuselage and wings real time processing power beyond sequential computers is required. Data needs to be measured, processed and corrective control action determined based on some mathematical model. This must be repeated at a suitable sample rate. To complicate matters further, the models relied upon can be in error due to parameter variation, modelling errors, etc. It is hence usually necessary to model the structure on-line and perform detailed simulations before control can be attempted. It therefore seems that classical computing techniques are inappropriate for dealing with control problems of this magnitude. In the paper the authors propose an alternative strategy whereby the potential of parallel processing networks is exploited. The authors concentrate on the simulation side of the problem and for convenience restrict attention to a cantilever systems since this covers several control applications. Such structures are governed by fourth order PDEs that need to be numerically solved for control purposes. The authors present a new parallel processing technique for solving such PDEs on transputer networks.< >

关键词： Simulation Large-scale systems Parallel processing Partial differential equations

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Classifier systems for control

Classifier systems for control

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iee colloquium on Genetic Algorithms for control systems Engineering

作者： T.C. Fogarty University of West of England Bristol UK

Classifier systems lie midway between neural networks and symbolic processing systems and potentially combine the benefits of both. They are parallel message-passing rule-based systems which use genetic algorithms to discover new rules as well as providing for reinforcement learning and programming. It has been proposed that a suitable application of genetic algorithms is to evolve robots. A most suitable way to use genetic algorithms to evolve the control systems for robots is within the framework provided by classifier systems. At a SERC workshop on learning systems a number of groups presented successful applications of the genetic algorithm to control problems. However, one cannot evolve complex systems with a simple genetic algorithm nor is it wise or safe to start from scratch in real applications where programmed knowledge can provide constraints for the genetic algorithm to work within. If the genetic algorithm is to be used to evolve control systems for industrial or commercial applications one of the best ways to do this is within the framework of classifier systems.< >

关键词： Genetic algorithms Knowledge based systems Pattern recognition Robots

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Using Xilinx FPGAs to implement neural networks and fuzzy systems

Using Xilinx FPGAs to implement neural networks and fuzzy sy...

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iee colloquium on neural and Fuzzy systems: Design, Hardware and Applications (Digest No: 1997/133)

作者： J.J. Blake L.P. Maguire T.M. McGinnity L.J. McDaid Faculty of Engineering University of Ulster Londonderr UK

Over the last thirty years, since Zadeh first introduced fuzzy set theory, there has been widespread interest in the real-time application of fuzzy logic, particularly in the area of control. Recently, there has been considerable interest in the development of dedicated hardware implementations which facilitate high speed processing. However, the main drawback of such an approach is that it is only cost effective for high-volume applications. A more feasible methodology for lower volume problems demands the application of general-purpose or programmable hardware such as the Xilinx FPGAs. There has been a similar trend in the area of neural networks, as initial research employed software simulations but more recent interest has investigated hardware implementations. FPGAs are becoming increasingly popular for prototyping and designing complex hardware systems. The structure of an FPGA can be described as an "array of blocks" connected together via programmable interconnections. The main advantage of FPGAs is the flexibility that they afford. An engineer can change and refine a device's design by exploiting the device's reprogrammability. Xilinx introduced the world's first FPGA, the XC2064, in 1985. This contained approximately 1000 logic gates. Since then, the gate density of Xilinx FPGAs has increased 25 times. There has been a lot of recent interest in the FPGA realisation of fuzzy systems. Similarly there are a number of FPGA implementations of neural networks reported in the literature. However. this paper provides a report on the implementation of both architectures and also offers a comparison with the hybrid structure.

关键词： neural networks

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Detection of brain conditions from evoked responses using artificial neural networks

Detection of brain conditions from evoked responses using ar...

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iee colloquium on Artificial Intelligence Methods for Biomedical Data Processing

作者： B.W. Jervis T. Roberts A. Lacey R. Woodcock S. Yu R.F. Harrison C.P. Lim E.M. Allen S. Oke School of Engineering Sheffield Hallam University Sheffield UK Department of Automatic Control and Systems Engineering University of Sheffield Sheffield UK Department of Clinical Neurophysiology Derriford Hospital Plymouth UK Mental Health Services Barrow Hospital Bristol UK

The contingent negative variation (CNV) is a cognitive event-related potential elicited by the presentation of a warning stimulus followed by an imperative stimulus. neural nets trained on CNV data offer an additional tool for the diagnosis of Huntington's Disease, Parkinson's Disease and schizophrenia, and may also offer a means of detecting and monitoring the pre-onset of Huntington's Disease. In future, it may be possible to quantify and monitor symptoms and to sub-classify the conditions, thus leading to improved drug treatment and patient care. Application of the fuzzy ARTMAP is most promising in this domain.

关键词： Brain Bioelectric potentials Electroencephalography Medical treatment Biomedical monitoring ART neural networks Fuzzy neural networks Biomedical signal processing

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Self-tuning PID control structures

Self-tuning PID control structures

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iee colloquium on Getting the Best Our of PID in Machine control

作者： P.J. Gawthrop Centre for Systems and Control & Department of Mechanical Engineering University of Glasgow Glasgow UK

There has been much work on self-tuning PID controllers. There are many possible structures for PID controllers. The basic concepts of PID control can be generalised within the same structure but allowing for the control of complicated dynamic systems using advanced control design algorithms. This structure arises naturally from the system description and does not need to be imposed artificially. The concept of PID control can be usefully generalised to make contact with recent methods such as internal-model control and generalised predictive control. One should not start with a PID controller and then decide how to tune it, but rather one should start with a rational design method and system model from which a (generalised) PID structure will then materialise. Recent advances in local model networks using multiple-model self-tuning PID controllers give a neat extension of the basic (generalised) PID structure to handle nonlinear or time-varying systems. The PID concept is alive and well.

关键词： Proportional control

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Artificial intelligence approaches to model-based control

Artificial intelligence approaches to model-based control

引用

iee colloquium on Update on Developments in Intelligent control (Ref. No. 1998/513)

作者： G.W. Irwin Advanced Control Engineering Centre Department of Electrical and Electronic Engineering Queen''s University Belfast Belfast UK

While of undoubted value for nonlinear identification and control of dynamic systems, neural networks have a number of limitations for practical applications. Thus, in online training, due consideration must be given to the necessity for regularisation with noisy data and to the choice of network architecture. More fundamentally, the nontransparent black-box nature of neural models make it difficult to include a priori system information, and to interpret the final structure meaningfully in terms of physical process characteristics. neural approaches also fail to exploit the significant body of theoretical results available for conventional model-based control, making it difficult to analyse the closed-loop behaviour in terms of stability and robustness. The aim of this paper is to describe a nonlinear modelling architecture, called the local model network (LMN), which introduces transparency while offering distinct advantages for nonlinear model-based control. Simulation results for a pH neutralisation process are used to illustrate the performance benefits of LMNs for nonlinear dynamic matrix control (DMC) and for nonlinear internal model control (IMC).

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Some developments towards analogue CMOS VLSI design

Some developments towards analogue CMOS VLSI design

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iee colloquium on VLSI Analogue Design

作者： D.G. Haigh J.T. Taylor K. Steptoe Department of Electronic and Electrical Engineering University College London UK

Recently, the possibility of 'real' analogue VLSI has begun to emerge. A system of this type may be loosely conceived of as a large chip, consisting primarily of high density analogue circuitry with a relatively smaller portion of digital circuitry for memory and control. In order to meet this challenge, it is clearly necessary to have at one's disposal, realisations for the basic system elements (or cells, e.g. operational amplifiers) which occupy small chip area, have low power consumption and still feature high precision performance. By adopting a firmly bottom-up approach, the authors examine different detailed aspects of analogue IC design which could help to bring the reality of analogue VLSI systems closer. These include continuous-time and discrete-time systems. They focus on minimisation of chip area and power consumption while maintaining high precision performance and consider complex analogue systems including artificial neural networks.< >

关键词： Design automation CMOS integrated circuits Integrated circuit fabrication Analog integrated circuits neural networks Switched capacitor circuits Very-large-scale integration

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