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adaptive non-linear filtering of ECG signals: Dynamic neural network approach

Adaptive non-linear filtering of ECG signals: Dynamic neural...

引用

Proceedings of the 1996 iee colloquium on Artificial Intelligence Methods for Biomedical Data Processing

作者： Yilmaz, A. English, M.J. Univ of Sussex Brighton United Kingdom

This paper describes some aspects of using dynamic neural networks in predictive type ECG filtering in comparison with adaptive linear filters. Two new algorithms are introduced and their performance is compared with normal and temporal backpropagation algorithms in terms of both the signal to noise ratio and the signal quality. The results indicate that using variant step size in learning algorithms improves the signal quality.

关键词： Electrocardiography

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adaptive non-linear filtering of ECG signals: dynamic neural network approach

Adaptive non-linear filtering of ECG signals: dynamic neural...

引用

iee colloquium on Artificial Intelligence Methods for Biomedical Data Processing

作者： A. Yilmaz M.J. English University of Sussex Brighton UK

This paper describes some aspects of using dynamic neural networks in predictive-type ECG filtering in comparison with adaptive linear filters. Two new algorithms are introduced and their performance is compared with normal and temporal backpropagation algorithms in terms of both the signal-to-noise ratio and the signal quality. The results indicate that using variant step size in learning algorithms improves the signal quality.

关键词： Electrocardiography Biomedical signal processing adaptive filters nonlinear filters neural networks Backpropagation

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Proceedings of the iee colloquium on non-linear Filters

Proceedings of the IEE Colloquium on Non-Linear Filters

引用

Proceedings of the iee colloquium on non-linear Filters

The proceedings contains 8 papers. Some of the topics discussed are: signal filtering;non-linear filters;digital signal processing;image processing;adaptive systems;neural networks;mathematical and statistical methods... 详细信息

关键词： Signal filtering and prediction

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A critical assessment of recurrent artificial neural networks as adaptive equalizers in digital communications

A critical assessment of recurrent artificial neural network...

引用

iee colloquium on Applications of neural networks to Signal Processing (Digest No. 1994/248)

作者： M.J. Bradley P. Mars School of Engineering University of Durham Durham UK

A number of neural network structures have previously been applied to the problem of equalization of digital communications channels and view the problem as one of pattern classification rather than one of inverse filtering. The recurrent neural network (RNN) has previously been shown to outperform the conventional linear transversal equalizer structure and has the advantage of requiring a small number of nodes to achieve a given level of equalization. The paper aims to highlight the mechanism by which RNNs equalize channels and to show that the dynamics of such networks create a structure unsuitable for reliable equalization.

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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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adaptive neural network control of the temperature in an oven

Adaptive neural network control of the temperature in an ove...

引用

iee colloquium on Advances in neural networks for Control and Systems

作者： O. Dubois J. Nicolas A. Billat UFR Sciences Exactes et Naturelles L. A. M Reims France

Many authors have shown by simulated studies, that a great number of non-linear dynamical systems could be identified and controlled by using neural network models. The authors applied these results to a real process : an oven with two inputs, one for heating and one for cooling. The output to be controlled is the temperature inside the oven. The choice of the control strategy on the one hand and the choice of the neural network architecture for the oven identification on the other hand had to satisfy two main objectives. First, the control strategy should be quite insensible to random disturbances (air leaks, door openings, ...) and the neural model should be able to fit any modification of the plant dynamics during its lifetime (modification of the internal load alteration of the heating system ...). The authors chose to use the internal model control as their control strategy. They also used a radial basis function network to identify the plant. The process identification is composed of two phases, an off-line one and an online one. The off-line part consists first in training the network while determining its internal structure using an initial training set. The on-line phase is the adaptive part of the control scheme.< >

关键词： adaptive control Feedforward neural networks Identification neural network architecture Process control Temperature control

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Application of artificial neural networks to medical signal processing

Application of artificial neural networks to medical signal ...

引用

iee colloquium on Applications of neural networks to Signal Processing (Digest No. 1994/248)

作者： J. Pardey S. Roberts L. Tarassenko The Medical Engineering Unit University of Oxford UK The Centre for Biological & Medical Systems Imperial College of Science Technology and Medicine London UK

The dynamics of human sleep have previously been examined using unsupervised clustering techniques (Roberts and Tarassenko, 1992). This culminated in the hypothesis that the structure of sleep can be described as a linear combination of three underlying processes. These correspond to the conventional, rule-based stages of wakefulness, REM sleep, and the deepest form of non-REM sleep, stage 4. The mixing fractions, p(W), p(R), and p(S), of these three processes vary as sleep progresses, and to estimate them a system has been developed that comprises an autoregressive (AR) model (Makhoul, 1975, Kay and Marple, 1981) followed by two artificial neural networks: a multi-layer perceptron (MLP) and a radial basis function (RBF) network, operating in parallel. The AR model is used to pre-process the EEG on a second-by-second basis, while the mixing fractions for each second are then estimated using the neural networks. The system is currently undergoing clinical trials, during which time the performance of the MLP and RBF networks will be assessed and a choice made as to which one to retain in the final, commercial system.

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iee colloquium on 'adaptive filtering, non-linear dynamics and neural networks' (Digest No.176)

IEE Colloquium on 'Adaptive Filtering, Non-Linear Dynamics a...

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iee colloquium on adaptive filtering, non-linear dynamics and neural networks

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Algorithms and architectures for nonlinear prediction and filtering

Algorithms and architectures for nonlinear prediction and fi...

引用

iee colloquium on adaptive filtering, non-linear dynamics and neural networks

作者： D.S. Broomhead LDRA(Electronics Division) RSRE Malvern Great Malvern UK

Summary form only given. The author uses a particular example to consider the relationship between adaptive filtering, nonlinear dynamics and neural networks. The example is an application of recent developments in nonlinear dynamical systems theory to signal processing based on the radial basis function least squares processor.< >

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adaptive networks, dynamical systems, and the predictive analysis of time series speech analysis

Adaptive networks, dynamical systems, and the predictive ana...

引用

1989 First iee International Conference on Artificial neural networks, (Conf. Publ. No. 313)

作者： D. Lowe A. Webb Royal Signals and Radar Establishment UK

The authors attempt to illustrate that adaptive network techniques provide an efficient mechanism for extracting qualitative details concerning the statistics and dynamics of transient time series based on a limited amount of information. Although the reported results on speech waveforms were obtained using a traditional multilayer perceptron structure (with linear output units), very similar results were obtained with radial basis function networks with spherical Gaussian nonlinearities at the hidden units. They suggest that the observed structure is characteristic of the data itself, as opposed to an artifact of the particular network used to model the observation sequence. They also suggest that this approach indicates fruitful possibilities for coding applications.< >

关键词： Encoding filtering Information theory Speech processing Time series

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