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Electronic nose: Clinical diagnosis based on soft computing methodologies 1

Electronic nose: Clinical diagnosis based on soft computing ...

引用

1st International ieeE Symposium on Intelligent systems

作者： Kodogiannis, VS Chountas, P Pavlou, A Petrounias, I Chowdrey, HS Temponi, C Univ Westminster Mechatron Grp Dept Comp Sci London HA1 3TP England

ISBN: (纸本)0780376013

Recently, the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence. It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage and among others, urine volatile compounds have been identified as possible diagnostic markers. A newly developed electronic nose based on chemoresistive sensors has been employed to identify in vitro 13 bacterial clinical isolates, collected from patients diagnosed with urinary tract infections, gastrointestinal and respiratory infections, and in vivo urine samples from patients with suspected uncomplicated UTI who were scheduled for microbiological analysis in a UK Health Laboratory environment. An intelligent model consisting of an odour generation mechanism, rapid volatile delivery and recovery system, and a classifier system based on a neural networks, genetic algorithms, and multivariate techniques such as principal components analysis and discriminant function analysis-cross validation. The experimental results confirm the validity of the presented methods.

关键词： neural networks genetic algorithms electronic noses microbial analysis

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neural networks and multivariate SPC

Neural networks and multivariate SPC

引用

iee colloquium on Fault Diagnosis in Process systems

作者： D.J.H. Wilson G.W. Irwin G. Lightbody Advanced Control Engineering Research Group Department of Electrical and Electronic Engineering Queen''s University Belfast Ireland

Recent developments in the instrumentation of plants has led to multivariate statistical process control (MSPC) techniques becoming increasingly popular for process monitoring in the chemical industry over the last few years. This paper examines one such algorithm, the partial least squares (PLS), and shows how the basic principles of this linear technique can be extended into the nonlinear domain via the application of radial basis function (RBF) neural networks. Results showing the successful application of these methods to fault detection in a validated model of an industrial overheads condenser and reflux drum plant are also given.

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Bottom profiling control of an UUV using neural networks

Bottom profiling control of an UUV using neural networks

引用

iee colloquium on control and Guidance of Underwater Vehicles

作者： C. Johnson R. Sutton G.N. Roberts Marine Dynamics Research Group University of Plymouth Plymouth Devon UK

Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVs). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning over that of the more commonly employed backpropagation algorithm. The results show for differing sized MLPs the chemotaxis algorithm produces a successful controller over the sea bed profile in an improved training time. To further vindicate the chemotaxis network, it was then presented with the problem of meeting a new profile to travel over. The results show from several simulation runs that the chemotaxis network provides a robust controller over numerous sea bed profiles of which it had no prior knowledge.< >

关键词： Feedforward neural networks Mobile robots Nonlinear systems

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Investigation of non-algorithmic software in flight control

Investigation of non-algorithmic software in flight control

引用

iee colloquium on Expert systems and Safety

作者： P.D. Houghton FS3 Division Royal Aerospace Establishment Bedford UK

Current implementations of aircraft flight control systems are an amalgam of numerous, discrete algorithms and various switching mechanisms. The switching mechanisms bring the algorithms into play either independently or in some form of combination. Advanced computing techniques, such as expert systems, can play a role by separating out the algorithm scheduling and making it more visible. By lessening the complexity both the integrity and capability should be improved. An alternative and more controversial approach is to use adaptive, learning and optimisation techniques such as neural networks. These offer advantages by removing the need for algorithm creation through learning, provide graceful degradation when elements fail and can adapt to subsystem failures. However at present this approach is not welcomed as conventional verification and validation techniques cannot be employed. The author discusses the role of non-algorithmic software.< >

关键词： Expert systems neural networks

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Integration and implementation of a priori knowledge for dynamics modelling and control

Integration and implementation of a priori knowledge for dyn...

引用

iee colloquium on Automotive Applications of Advanced Modelling and control

作者： K.J. Hunt Systems Technology Research Berlin Daimler Benz Aerospace Germany

neural networks have been employed in a range of automotive modelling and control tasks. This paper discusses the recently introduced local model network structure. The structure can be viewed as a hybrid model which smoothly combines local models (of linear or nonlinear form) by interpolation. Interpolation of local models is implemented in the form of a basis-function network. The benefits of this structure include: existing knowledge can be directly integrated, and the model can be fully analysed and interpreted in a local manner. The author describes the local model network and discusses its relevance for automotive modelling and control tasks.< >

关键词： Road vehicles Dynamics Interpolation neural networks

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Neuro-fuzzy networks for process modelling and model-based control

Neuro-fuzzy networks for process modelling and model-based c...

引用

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

作者： Jie Zhang J. Morris Centre for Process Analysis Chemomatrics and Control Department of Chemical & Process Engineering University of Newcastle Newcastle-upon-Tyne UK

The article describes the concept and performance of neuro fuzzy modelling and neuro fuzzy model based control. The technique has been applied to a highly nonlinear neutralisation process. It is shown that the neuro fuzzy model is easy to interpret and provides accurate predictions. The proposed neuro fuzzy model based control strategy utilises local incremental models which can naturally eliminate steady state offsets. It is shown that the neuro fuzzy model based controller gives much improved performance compared to that of a linear model based controller.

关键词： Neurocontrollers

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Fault diagnosis of a nuclear processing plant at different operating points using neural networks

Fault diagnosis of a nuclear processing plant at different o...

引用

iee colloquium on Fault Diagnosis in Process systems

作者： M. Weerasinghe J.B. Gomm D. Williams Control Systems Research Group School of Electrical Engineering and Electronics John Moores University Liverpool UK

The process under investigation in this work is the integrated dry route (IDR) process of British Nuclear Fuels plc. (BNFL), which is a nuclear fuel processing plant, where non-catastrophic faults are known to occur and a reliable early fault diagnosis scheme was required for operator advice. This paper describes the application of artificial neural network techniques to the diagnosis of non-catastrophic faults in the IDR process which operates at a few different operating points. The techniques involved developing methods to preprocess the data by statistical scaling, reducing the neural network input space using principal component analysis and training and testing the neural networks. Results are presented to illustrate the performance of the developed scheme on application to the IDR process data.

关键词： Nuclear fuels

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The simulation study of robust fault detection approaches on a hydraulic test rig

The simulation study of robust fault detection approaches on...

引用

iee colloquium on Fault Diagnosis and control System Reconfiguration

作者： D. Yu D.N. Shields J.L. Mahtani Control and Applications Centre Coventry University Coventry UK

Describes the results of a simulation study of two fault detection approaches applied to a hydraulic test rig: an eigenstructure assignment approach; and a neural network based approach. The rig has a predominant natural frequency of the order of 20 Hz and is more representative of real industrial systems due to the presence of operating point nonlinearity, measurement noise and load disturbances.< >

关键词： Eigenvalues and eigenfunctions Fault location Hydraulic equipment neural networks Test equipment

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On stability theory for nonlinear control schemes with intelligent networks

On stability theory for nonlinear control schemes with intel...

引用

iee colloquium on Non-Linear control

作者： E. Rogers Adv. Syst. Res. Group Southampton Univ. UK

Learning methods are currently of great interest to (sections) of the control systems community. There is an ever increasing volume literature on neural networks. At best, this work has shown that, appropriately chosen intelligent control type schemes, or architectures are capable of synthesising control laws for partially known systems. A large class of these approaches employ iterative techniques to adjust the variable parameters of the architecture and hence develop discrete time models of the inverse dynamics of the process to be controlled. The resulting trained network is then used as the basis of a nonlinear control law. It is important to note, however, that this general approach has several major drawbacks. These drawbacks are some of the fundamental reasons why the adaptive control community moved away from gradient based methods in favour of a formal stability theory. Currently, a number of research groups, including The Advanced systems Research Group at Southampton, are investigating the feasibility of analogue network designs for the control of continuous time nonlinear dynamic systems. One idea which has already received considerable attention is to combine an adaptation law with a sliding mode, or variable structure controller to give a globally stable closed-loop system with good tracking properties. The presentation reviews progress to-date with particular emphasis on stability theory. Some ongoing work and areas for short to medium term further development are also briefly outlined.< >

关键词： Adaptive control Intelligent control neural networks Nonlinear systems Stability

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Structural generalization in neural classification: incorporation of prior probabilities

Structural generalization in neural classification: incorpor...

引用

iee colloquium on Adaptive Filtering, Non-Linear Dynamics and neural networks

作者： D.W. McMichael Control Systems Centre University of Manchester Institute of Science and Technology Manchester UK

Supervised learning of classifications under l/sub 2/ costing of error trains learning classifiers to recall Bayesian a posteriori probabilities of the possible classes, given observed measurements. This result leads to a number of insights concerning the validation of training, access to the likelihood function, creating networks of networks, incorporation of prior probabilities (which may vary in real-time), and how to choose the training set. The author focuses on the latter two points. Contextual information in the form of priors is used to generalise training data to economise on both training and computation. Structural generalization is the process whereby data is generalised architecturally rather than parametrically. A training procedure and postprocessing technique are given which enable learning under one set of prior classification probabilities to be generalized to give (asymptotically) Bayes optimal classifications under all others.< >

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