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991.
Multivariate statistical process control based on multiway locality preserving projections 总被引:1,自引:0,他引:1
An approach for multivariate statistical process control based on multiway locality preserving projections (LPP) is presented. The recently developed LPP is a linear dimensionality reduction technique for preserving the neighborhood structure of the data set. It is characterized by capturing the intrinsic structure of the observed data and finding more meaningful low-dimensional information hidden in the high-dimensional observations compared with PCA. In this study, LPP is used to extract the intrinsic geometrical structure of the process data. Hotelling’s T2 (D) and the squared prediction error (SPE or Q) statistic charts for on-line monitoring are then presented, and the contribution plots of these two statistical indices are used for fault diagnosis. Moreover, a moving window technique is used for the implementation of on-line monitoring. Case study was carried out with the data of industrial penicillin fed-batch cultivations. As a comparison, the results obtained with the MPCA are also presented. It is concluded that the Multiway LPP (MLPP) outperforms the conventional MPCA. Finally, the robustness of the MLPP monitoring is analyzed by adding noises to the original data. 相似文献
992.
993.
A steady-state interval operability methodology is introduced here for multivariable non-square systems with fewer inputs than output variables to be used in the design of model-based constrained controllers (MPC, DMC). For such systems, set-point control is not possible for all the outputs and interval control is needed. The proposed iterative approach enables the selection of the needed interval constraints systematically, so that the tightest possible control is achieved without rendering the control problem infeasible. The application of this methodology to high-dimensional industrial problems characterizing processes of Air Products and Chemicals and DuPont shows that very significant reduction of the constrained region can be achieved from the steady-state point of view. Ratios of the initial to the calculated volume of the constrained regions examined range between 104 and 108. 相似文献
994.
This paper describes a new method for increasing the computational efficiency of nonlinear robust model-based predictive control. It is based on the application of neuro-fuzzy networks and improves the computation efficiency by arranging the online optimisation to be done offline. The offline optimisation is realized by offline training a neuro-fuzzy network, consisting of zero-order T–S fuzzy rules, which is designed to approximate the input–output relationship of a robust model-based predictive controller. The design and the training of the neuro-fuzzy network are described, and the corresponding control algorithm is developed. Experiment results performed on the temperature control loop of an experimental air-handling unit (AHU) demonstrate the effectiveness of this approach. 相似文献
995.
We present a stabilizing scheduled output feedback Model Predictive Control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local output feedback predictive controllers with their estimated regions of stability covering the desired operating region, and implement them as a single scheduled output feedback MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm provides a general framework for scheduled output feedback MPC design. 相似文献
996.
Statistical process control (SPC) is a conventional means of monitoring software processes and detecting related problems,
where the causes of detected problems can be identified using causal analysis. Determining the actual causes of reported problems
requires significant effort due to the large number of possible causes. This study presents an approach to detect problems
and identify the causes of problems using multivariate SPC. This proposed method can be applied to monitor multiple measures
of software process simultaneously. The measures which are detected as the major impacts to the out-of-control signals can
be used to identify the causes where the partial least squares (PLS) and statistical hypothesis testing are utilized to validate
the identified causes of problems in this study. The main advantage of the proposed approach is that the correlated indices
can be monitored simultaneously to facilitate the causal analysis of a software process.
Ching-Pao Chang is a PhD candidate in Computer Science & Information Engineering at the National Cheng-Kung University, Taiwan. He received his MA from the University of Southern California in 1998 in Computer Science. His current work deals with the software process improvement and defect prevention using machine learning techniques. Chih-Ping Chu is Professor of Software Engineering in Department of Computer Science & Information Engineering at the National Cheng-Kung University (NCKU) in Taiwan. He received his MA in Computer Science from the University of California, Riverside in 1987, and his Doctorate in Computer Science from Louisiana State University in 1991. He is especially interested in parallel computing and software engineering. 相似文献
Chih-Ping ChuEmail: |
Ching-Pao Chang is a PhD candidate in Computer Science & Information Engineering at the National Cheng-Kung University, Taiwan. He received his MA from the University of Southern California in 1998 in Computer Science. His current work deals with the software process improvement and defect prevention using machine learning techniques. Chih-Ping Chu is Professor of Software Engineering in Department of Computer Science & Information Engineering at the National Cheng-Kung University (NCKU) in Taiwan. He received his MA in Computer Science from the University of California, Riverside in 1987, and his Doctorate in Computer Science from Louisiana State University in 1991. He is especially interested in parallel computing and software engineering. 相似文献
997.
This paper investigates an intelligent adaptive control system for multiple-input–multiple-output (MIMO) uncertain nonlinear systems. This control system is comprised of a recurrent-cerebellar-model-articulation-controller (RCMAC) and an auxiliary compensation controller. RCMAC is utilized to approximate a perfect controller, and the parameters of RCMAC are on-line tuned by the derived adaptive laws based on a Lyapunov function. The auxiliary compensation controller is designed to suppress the influence of residual approximation error between the perfect controller and RCMAC. Finally, two MIMO uncertain nonlinear systems, a mass–spring–damper mechanical system and a Chua’s chaotic circuit, are performed to verify the effectiveness of the proposed control scheme. The simulation results confirm that the proposed intelligent adaptive control system can achieve favorable tracking performance with desired robustness. 相似文献
998.
Design of variable structure control for fuzzy nonlinear systems 总被引:1,自引:0,他引:1
In this paper, the variable structure control problem is presented for Takagi–Sugeno fuzzy systems with uncertainties and external disturbances. The sliding surfaces for the T–S fuzzy system are proposed by using a Lyapunov function and a fuzzy Lyapunov function, respectively. And we design the variable structure controllers such that the global T–S fuzzy system confined on the sliding surfaces is asymptotically stable. Two examples are given to illustrate the effectiveness of our proposed methods. 相似文献
999.
Chun-Liang Lin Chun-Hsiung Chen Bing-Min Shiu 《Journal of Intelligent Manufacturing》2008,19(4):407-419
In practical electro-pneumatic or hydraulic servo mechanism, there is usually an unavoidable operating delay time in the solenoid
valves. This may sometimes cause closed-loop instability if the time delay is not treated carefully in the control system
designs. Furthermore, pneumatic actuators can offer high performance at low cost, but are often suffered from external disturbances
depending on the operating conditions. This paper proposes a non-model-based design approach for pneumatic actuating systems.
In the proposed control system, a gain scheduled fuzzy-PID controller ensures tracking performance an adaptable wavelet neuro
compensator compensates for time-delay of the control valve, and a disturbance rejecter diminishes influence from load changes.
A condition ensuring the closed-loop stability is derived. The proposed design is experimentally verified to show its effectiveness. 相似文献
1000.
In the present and next generation wireless networks, cellular system remains the major method of telecommunication infrastructure. Since the characteristic of the resource constraint, call admission control is required to address the limited resource problem in wireless network. The call dropping probability and call blocking probability are the major performance metrics for quality of service (QoS) in wireless network. Many call admission control mechanisms have been proposed in the literature to decrease connection dropping probability for handoffs and new call blocking probability in cellular communications. In this paper, we proposed an adaptive call admission control and bandwidth reservation scheme using fuzzy logic control concept to reduce the forced termination probability of multimedia handoffs. Meanwhile, we adopt particle swarm optimization (PSO) technique to adjust the parameters of the membership functions in the proposed fuzzy logic systems. The simulation results show that the proposed scheme can achieve satisfactory performance when performance metrics are measured in terms of the forced termination probability for the handoffs, the call blocking probability for the new connections and bandwidth utilization. 相似文献