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Accurate forecasting of some economic indicators such as GDP is very useful. Aiming at the problem of modeling and forecasting of the nonlinear and complex economic system, an improved least square support machine model is proposed in this paper. A multi-scale chaotic search algorithm combined with GA is proposed for the optimum selection of model parameters. Time series data of the indicator to be...
Biological wastewater treatment is a nonlinear, time-varying and time-delay process. It is difficult to achieve precise control. In order to solve the instability problem of dissolved oxygen (DO) control, a method of DO generic model control (GMC) based on immune optimization least squares support vector machine (LS-SVM) is proposed. The method doesn't require model simplification and linearization...
In this paper, a new stable robust adaptive control approach is presented for SISO uncertain nonlinear system. The key assumption is that LS-SVM approximation errors and external disturbances satisfy certain bounding conditions. The LS-SVM can find a global minimum and avoid local minimum. Its weights in the observer can be tuned after training phase and find optimistic value automatically. By combining...
The design and implementation of a modern elevator group control system (EGCS) is introduced in this paper. The basic considerations of designing an EGCS are discussed, including related system parameters, evaluation criterions and traffic patterns. Least squares support vector machine algorithm is employed for traffic prediction. Using multi-support vector machine, the traffic pattern recognition...
Elevator traffic flow is fundamental in elevator group control systems. Accurate elevator traffic flow prediction is crucial to the planning and dispatching of elevator group control systems. Support vector machine (SVM) based on statistical learning theory has shown its advantage in regression and prediction. In this paper, we predict elevator traffic flow using least squares support vector machines...
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