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Clarifying process of sugar cane juice is a dynamic nonlinear system which has the characteristics of strong non-linearity, multi-constraint, large time-delay, multi-input and other characteristics of complex nonlinear systems. In this paper, Elman neural network is applied to the model of the clarifying process of sugar cane juice. An improved method of(More)
Adaptive critic designs is a method used to approximate optimal control over time in nonlinear systems, which is suitable for dealing with time-varying complex systems and dynamic varying complex tasks. On the other hand, the boiler combustion system is a typical plant with multiple inputs/multiple outputs, non-linearity and time-delay, and the coupling(More)
In view of the prediction accuracy of Extreme Learning Machine's (ELM) is affected by its input weights and hidden layer neurons thresholds, an improved training method for ELM with Genetic Algorithms (GA-ELM) is proposed in this paper. In GA-ELM, after selection, crossover and mutation of Genetic Algorithm (GA), we will get the optimal weights and(More)
To achieve a reasonable power split scheme of Li battery pack and supercapacitor hybrid electric vehicles, we propose dynamic programming (DP) based predictive control algorithm (PCA) in this paper. First, the model of the vehicle plant is established consisting of mathematical models of supercapacitor and Li battery pack. Then, the PCA based control system(More)
This paper presents a novel nonlinear optimal neurocontroller for a static compensator (STATCOM) connected to a power system. The design for the optimal controller is based on a class of Adaptive Critic Designs (ACDs) called the Action Dependant Heuristic Dynamic Programming (ADHDP). The ADHDP class of ACDs uses two neural networks, an(More)