In this paper, the projection identification algorithm is extended and the generalized projection algorithm is presented. The analysis indicates that the generalized projection algorithm can track the time-varying par...
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In this paper, the projection identification algorithm is extended and the generalized projection algorithm is presented. The analysis indicates that the generalized projection algorithm can track the time-varying parameters and has the same properties as the forgetting factor least squares algorithms but has less computational effort. The way of choosing the data window length is stated so as to obtain the minimum upper bound of the parameter estimation errors.
In order to solve the problem when monitoring and controlling penicillin fermentation processes,So an intelligent modeling method based on Quantum Particle Swarm optimization(QPSO) algorithm and Weighted Least Squares...
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In order to solve the problem when monitoring and controlling penicillin fermentation processes,So an intelligent modeling method based on Quantum Particle Swarm optimization(QPSO) algorithm and Weighted Least Squares Support Vector Machines(WLS-SVM) is presented,which can overcome the noise of sample data,the high *** the method in penicillin fermentation processes and compared with the Pensim simulation platform data,it obviously found that the WLS-SVM is superior to the unweighted LS-SVM modeling method that has a better estimation accuracy and robustness.
The input-to-state stability for a class of nonlinear switched descriptor systems is considered for two cases. Based on the average dwell time approach, sufficient conditions are derived to guarantee that the whole sy...
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The input-to-state stability for a class of nonlinear switched descriptor systems is considered for two cases. Based on the average dwell time approach, sufficient conditions are derived to guarantee that the whole system is input-tostate stable through designing an appropriate switching rule. Compared with the existing methods, it is more convenient to design the controller for each subsystem, because it does not require to construct the input-to-state stable control Lyapunov function and to design the specific structure of the control inputs. Finally, two numerical examples show that the results obtained in this paper are feasible and effective.
For a class of discrete time dynamical systems, an adaptive control scheme is proposed based on neural networks and multi-model. By designing a reasonable switching law among the models, the merits of linear robust ad...
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For a class of discrete time dynamical systems, an adaptive control scheme is proposed based on neural networks and multi-model. By designing a reasonable switching law among the models, the merits of linear robust adaptive controller and a neural networks based nonlinear adaptive controller can be well integrated, such that the best controller can be selected for the system at anytime. The control of stability and performance improving can achieve respectively, which not only guarantees the stability, but also improves the adaptive control performance by using neural network controller. Finally, it is demonstrated that improved performance and stability can be simultaneously achieved by simulation examples.
This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis...
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This paper proposes an artificial neural network (ANN) based time/space separation modeling approach to predict nonlinear parabolic DPSs. First, the spatial-temporal output is divided into a few dominant spatial basis functions and low-dimensional time series by PCA method. Then a three-layer feed-forward ANN is identified by low-dimensional time series, where the improved group search optimization (GSO) is proposed to optimize the connection weights and thresholds to solve the problem of falling into the local optima. Finally, the nonlinear spatiotemporal dynamics is determined after the time/space reconstruction. Simulations are presented to demonstrate the accuracies and effectiveness of the proposed methodologies.
This paper presents a simple analytical method for the design of full matrix PI controller based on the direct synthesis approach. By proposing the practically desired closed-loop diagonal transfer function to reduce ...
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This paper presents a simple analytical method for the design of full matrix PI controller based on the direct synthesis approach. By proposing the practically desired closed-loop diagonal transfer function to reduce interactions between individual loops, analytical expressions for PI controller are derived for several common types of process models, including first order plus time delay models and second order plus time delay models. Compared with the existing direct synthesis approaches, the proposed controller design method requires no approximation of the inverse of process model or Maclaurin's series expansion. Furthermore, it is applicable to high dimensional multivariable systems with satisfactory performance and robustness. Several examples are introduced to demonstrate the effectiveness and simplicity of the design method.
This paper considers the parameter estimation problems of the multivariable linear systems disturbed by moving average noises, which can be modeled by a multiple pseudolinear regression model. The recursive extended l...
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ISBN:
(纸本)9781467327626
This paper considers the parameter estimation problems of the multivariable linear systems disturbed by moving average noises, which can be modeled by a multiple pseudolinear regression model. The recursive extended least squares algorithm is presented for this class of multivariable systems, and then the performance of the proposed algorithm is studied. The analysis shows that the parameter estimates converge fast to their true values under weak conditions. Two simulation examples are given to illustrate the effectiveness of the algorithm.
In order to improve the estimation accuracy of a soft sensor in the chemicalprocess, an ensemble model is proposed based on Boosting and Gaussian process algorithms. Using Gaussian process as a base learner, a levera...
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In order to improve the estimation accuracy of a soft sensor in the chemicalprocess, an ensemble model is proposed based on Boosting and Gaussian process algorithms. Using Gaussian process as a base learner, a leveraging learner is constructed by Boosting algorithm. The ensemble model is obtained by dynamically averaging the regression functions trained by leveraging learners. Finally, the algorithm is applied to a soft sensor model for a production plant of Bisphenol A. Simulation results show that the integration algorithm has higher accuracy and generalization ability comparing to a single Gaussian process model.
An automatic sleep level estimation method was developed for monitoring and regulation of day time nap sleep. The recorded nap data is separated into continuous 5-second segments. Features are extracted from EEGs, EOG...
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ISBN:
(纸本)9781457702150
An automatic sleep level estimation method was developed for monitoring and regulation of day time nap sleep. The recorded nap data is separated into continuous 5-second segments. Features are extracted from EEGs, EOGs and EMG. A parameter of sleep level is defined which is estimated based on the conditional probability of sleep stages. An exponential smoothing method is applied for the estimated sleep level. There were totally 12 healthy subjects, with an averaged age of 22 yeas old, participated into the experimental work. Comparing with sleep stage determination, the presented sleep level estimation method showed better performance for nap sleep interpretation. Real time monitoring and regulation of nap is realizable based on the developed technique.
The problems of robust H∞ guaranteed cost control and feedback stabilization for a class of uncertain switched singular systems with time-varying delay are studied based on the Lyapunov function approach and convex c...
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The problems of robust H∞ guaranteed cost control and feedback stabilization for a class of uncertain switched singular systems with time-varying delay are studied based on the Lyapunov function approach and convex combination techniques. A sufficient condition for the existences of robust H∞ guaranteed cost controller and switching strategy are given, and the designed state feedback controllers in terms of linear matrix inequality ensure that the closed-loop systems satisfy the guaranteed cost index with a presented H∞ disturbance attenuation level γ. Finally, an illustrative example shows the effectiveness of the proposed method.
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