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Analysis of Nonlinear Partial Least Squares Algorithms

IFAC Proceedings Volumes 2004年第9期37卷 739-744页

作者： S. Kumar U. Kruger E.B. Martin A.J. Morris Centre of Process Analytics and Process Technology University of Newcastle NE1 7RU U.K. Intelligent Systems and Control Group Queen's University Belfast BT9 5AH U.K.

This paper presents an analysis of nonlinear extensions to Partial Least Squares (PLS) using error-based minimization techniques. The analysis revealed that such algorithms are maximizing the accuracy with which the response variables are predicted. Therefore, such algorithms are nonlinear reduced rank regression algorithms rather than nonlinear PLS algorithms

关键词： Nonlinear Systems Identification Algorithms Models Gradient Methods Prediction

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Adaptive partial least squares with application to process monitoring 5

Adaptive partial least squares with application to process m...

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5th IFAC Symposium on Fault Detection, Supervision and Safety of Technical processes, Safeprocess 2003

作者： Kumar, S. Thennadil, S.N. Morris, A.J. Martin, E.B. Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-TyneNE1 7RU United Kingdom

Partial least squares has been applied in process modelling and process performance monitoring where a large number of correlated variables are recorded. The most commonly implemented algorithm for identifying a PLS model has been the batch algorithm. In this case, data from the process is first required to be stored. In this paper an algorithm is proposed that recursively updates the parameters of PLS as and when a sample of measurements is obtained thereby reducing the need for data storage. The algorithm was tested on an artificially generated data set. The approach was then used to derive a statistic for process monitoring that was applied for the detection of faults in a continuous stirred tank reactor. © 2003 International Federation of Automatic control.

关键词： Fault detection

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Neural network model based batch-to-batch optimal control

Neural network model based batch-to-batch optimal control

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IEEE International Symposium on Intelligent control (ISIC)

作者： Jie Zhang Centre for Process Analytics and Control Technology School of Chemical Engineering & Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

A neural network based batch to batch optimal control strategy is proposed in this paper. To overcome the difficulty in developing mechanistic models for batch processes, neural network models are developed from process operational data. The developed neural network model can only approximate the batch process and model plant mismatches usually exist. Thus the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch to batch optimal control strategy based on the linearization of the neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.

关键词： Neural networks Optimal control Manufacturing processes Polymers Inductors Chemical analysis process control Chemical technology Chemical engineering Electronic mail

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Improving data based nonlinear process modelling through Bayesian combination of multiple neural networks

Improving data based nonlinear process modelling through Bay...

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International Joint Conference on Neural Networks (IJCNN)

作者： Z. Ahmad Jie Zhang Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

A single neural network model developed from a limited amount of data usually lacks robustness. Thus combining multiple neural networks can enhance the neural network model performance. In this paper, a Bayesian combination method is developed for nonlinear dynamic process modelling and compared with simple averaging. Instead of using fixed combination weights, the estimated probability of a particular network being the true model is used as the combination weight for combining that network. A nearest neighbour method is used in estimating the network error for a given input data point, which is then used in calculating the combination weights for individual networks. The prior probability is estimated using the SSE of individual networks on a sliding window covering the most recent sampling times. It is shown that Bayesian combination generally outperforms simple averaging.

关键词： Bayesian methods Neural networks Robustness Training data process control Robust control Artificial neural networks Industrial training Chemical analysis Chemical technology

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Multi-objective optimal control of batch processes using recurrent neuro-fuzzy networks

Multi-objective optimal control of batch processes using rec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jie Zhang Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780378989

A recurrent neuro-fuzzy network based strategy for batch process modelling and multi-objective optimal control is presented. In this recurrent neuro-fuzzy network a "global" nonlinear long-range prediction model is constructed from the fuzzy conjunction of a number of "local" linear dynamic models. The network output is fed back to the network input through one or more time delay units. This particular structure ensures that predictions from a recurrent neuro-fuzzy network are long-range or multi-step-ahead predictions. process knowledge is used to initially partition the process nonlinear characteristics into several local operating regions and to aid in the initialisation of the corresponding network weights. process input output data is then used to train the network. Membership functions of the local regimes are identified and local models are discovered through network training. In this paper, a recurrent neuro-fuzzy network is used to model a fed-batch reactor and to calculate the optimal feeding policy.

关键词： Optimal control Fuzzy neural networks Predictive models Neural networks Inductors process control Delay effects Manufacturing processes Polymers Chemical analysis

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Batch-to-batch model-based iterative optimisation control for a batch polymerisation reactor

Batch-to-batch model-based iterative optimisation control fo...

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American control Conference (ACC)

作者： Zhihua Xiong Jie Zhang Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-Tyne UK

ISBN: (纸本)0780378962

A batch-to-batch model-based iterative optimisation control strategy for a batch polymerisation reactor is proposed. Because the reference sequences for product quality variables during a batch are usually difficult to be set reasonably and the interest usually lies in the end-of-batch product quality, a modified quadratic objective function is introduced to track the desired qualities at the end-point of a batch. Predictions of recurrent neural network models are modified using the prediction errors in the previous runs. Because model errors are gradually reduced from batch to batch, the control trajectory gradually approaches to the optimal control policy. The proposed scheme is illustrated on a simulated batch polymerisation reactor.

关键词： Polymers Inductors Recurrent neural networks Predictive models Error correction Optimal control Temperature control Chemical analysis process control Chemical technology

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Adaptive Partial Least Squares with Application to process Monitoring

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IFAC Proceedings Volumes 2003年第5期36卷 789-794页

作者： S. Kumar S.N. Thennadil A.J. Morris E.B. Martin Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU England UK

关键词： Subspace methods Adaptive systems Fault detection

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Neural network based on-line re-optimisation of fed-batch processes using iterative dynamic programming for discrete-time systems 15th

Neural network based on-line re-optimisation of fed-batch pr...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Xiong, Zhihua Zhang, Jie Centre for Process Analytics and Control Technology Department of Chemical and Process Engineering University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

Optimisation of fed-batch processes can be described as a constrained nonlinear end-point dynamic optimisation problem. Although iterative dynamic programming (IDP) is feasible, it is usually very time-consuming and very difficult to apply to on-line optimisation because of solving the non-linear differential-algebraic equations of the process model in each iteration. The replacement of a rigorous mechanistic model by an equivalent neural network (NN) model takes the advantage of high speed processing, since simulation with a NN model involves only a few non-iterative algebraic calculations. To use IDP algorithm for NN model based on-line re-optimisation, a modified algorithm is proposed and is called as iterative dynamic programming for discrete-time system (IDP/DTS). The novel IDP/DTS algorithm can obtain a reduction of many times in computational time compared to the conventional IDP algorithm. In this paper, an effective optimisation and control scheme for on-line re-optimisation of fed-batch processes is proposed based on NN models and the novel IDP/DTS algorithm. The proposed scheme is illustrated using simulation studies of an ethanol fermentation process. Copyright © 2002 IFAC.

关键词： Dynamic programming

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Local linear and non-linear multi-way partial least squares batch modelling 15th

Local linear and non-linear multi-way partial least squares ...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Fletcher, N.M. Morris, A.J. Martin, E.B. Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

An industrial fed-batch fermentation process forms the basis of a study that illustrates the development and use of local models for the construction of a performance monitoring scheme. Linear models are initially built for different operating regions within the batch using Dynamic Partial Least Squares (DPLS) to capture the underlying process dynamics. The local models are then pieced together and a global model developed based on the latent variable scores. A comparison with a global non-linear quadratic DPLS model is then made prior to using the latent variable scores from the local models to build a nominal process representation. Copyright © 2002 IFAC.

关键词： Least squares approximations

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Detection of process model change in PLS based performance monitoring 15th

Detection of process model change in PLS based performance m...

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15th World Congress of the International Federation of Automatic control, 2002

作者： Kumar, Sukhbinder Martin, Elaine B. Morris, Julian Centre for Process Analytics and Control Technology Merz Court University of Newcastle Newcastle upon TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661746

The detection of process changes using a partial least squares (PLS) based monitoring scheme can be achieved through the interrogation of two metrics, Hotelling's T and the Q-statistic. The Q-statistic has been shown to be insensitive to small changes in the process model parameters. In this paper, a modified statistic based on the local approach is proposed to detect changes in the model parameters in a PLS based monitoring scheme. The performance of the Q-statistic is compared with the modified statistic through their application to fault detection in a continuous stirred tank reactor. Copyright © 2002 IFAC.

关键词： Fault detection

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