In order to implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In PSODE, control parameters are encoded to be a symbi...
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In order to implement self-adaptive control parameters, a hybrid differential evolution algorithm integrated with particle swarm optimization (PSODE) is proposed. In PSODE, control parameters are encoded to be a symbiotic individual of original individual, and each original individual has its own symbiotic individual. Differential evolution operators are applied to evolve the original population. And, PSO is applied to co-evolve the symbiotic population. Thus, with the evolution of the original population in PSODE, the symbiotic population is dynamically and self-adaptively adjusted and the real-time optimum control parameters are obtained. To illustrate the performance of PSODE, DE/rand/1, DE/best/1, DE/rand-to-best/1, DE/rand/2, DE/best/2, self-adaptive Pareto DE (SPDE), self-adaptive DE (SDE) and PSODE are applied to optimize 9 benchmark functions. The results show that the average performance of PSODE is the best.
In order to extract from the video sequence in a complete and consistent moving target, a novel algorithm for video object segmentation based on improved particle swarm optimization (IPSO) is presented. The algorithm ...
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For the gasoline pipeline blending process, recipe optimization system is greatly dependent on the near-infrared spectroscopy online analyzer, whose spectral model plays an important role in the measurement. The sepec...
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For the gasoline pipeline blending process, recipe optimization system is greatly dependent on the near-infrared spectroscopy online analyzer, whose spectral model plays an important role in the measurement. The sepectral model's accuracy and adaptability directly affect the applicability of the entire online blending system. This paper studies how to establish model for gasoline octane number for the gasoline pipeline blending process with near-infrared spectroscopy online analyzer. It is proposed using principal component analysis (PCA) together with Artificial Neural Network (ANN) method to establish spectral-model for octane number. Multivariate linear regressions(MLR) and partial least squares (PLS) method have also been used to establish gasoline octane model for comparison purpose. The results show that the model established by PCA and ANN has strong anti-jamming capability and suitable for gasoline online blending application.
The flow shop scheduling problems with zero wait is considered as one of the most challenging problems in the field of scheduling. This paper deals with the problem considering the makespan minimization as the objecti...
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This paper investigates the problem of event-triggered dual-mode distributed predictive control(DPC) for constrained large-scale linear systems subject to bounded *** on input-to-state stability(ISS) theory,the event-...
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ISBN:
(纸本)9781509009107
This paper investigates the problem of event-triggered dual-mode distributed predictive control(DPC) for constrained large-scale linear systems subject to bounded *** on input-to-state stability(ISS) theory,the event-triggering condition involving information of the subsystem itself is derived.A dual-mode predictive control scheme is designed to reduce information exchanges with neighboring *** upper bound of disturbances for ensuring the recursive feasibility and closed-loop stability are ***,a simulation example is given to show that the presented method is able to save computation resources and communication resources while guaranteeing the desired control performance.
Conventional principal component analysis (PCA) can obtain low-dimensional representations of original data space, but the selection of principal components (PCs) based on variance is subjective, which may lead to inf...
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Sleep staging has important significance for the monitoring, prevention and treatment of sleep disorders. In this study, the clustering algorithm of K-means is investigated in order to realize the automatic sleep stag...
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The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but m...
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The kernel principal component analysis （KPCA） method employs the first several kernel principal components （KPCs）, which indicate the most variance information of normal observations for process monitoring, but may not reflect the fault information. In this study, sensitive kernel principal component analysis （SKPCA） is proposed to improve process monitoring performance, i.e., to deal with the discordance of T2 statistic and squared prediction error SVE statistic and reduce missed detection rates. T2 statistic can be used to measure the variation di rectly along each KPC and analyze the detection performance as well as capture the most useful information in a process. With the calculation of the change rate of T2 statistic along each KPC, SKPCA selects the sensitive kernel principal components for process monitoring. A simulated simple system and Tennessee Eastman process are employed to demonstrate the efficiency of SKPCA on online monitoring. The results indicate that the monitoring performance is improved significantly.
In this paper, an emerging artificial neural network (ECANN) is proposed. Abstracting from a latest research in neuroscience, electromagnetic coupling among neuron activities is introduced into the model. Besides, the...
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Since all of adaptive models converge to the same point in parameter space,classical second level multiple model adaptive control method cannot deal with linear time-varying *** order to deal with this drawback,this p...
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ISBN:
(纸本)9781509046584
Since all of adaptive models converge to the same point in parameter space,classical second level multiple model adaptive control method cannot deal with linear time-varying *** order to deal with this drawback,this paper proposes a modified second level adaptive control method based on combining the advantages of adaptive model and fixed model for a class linear discrete time-varying plant where only a little prior information which is ***,multiple adaptive models are selected in the space of parameters,and the parameters are identifying and the controller is designed based on second level adaptive ***,when one of sub-model of the adaptive models are closed to the real parameters of the system,all of these adaptive models are fixed,and then the controller is designed based on multiple fixed ***,simulations have demonstrated that this method is effectively to a class of linear time-varying system.
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