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This paper proposes a hybrid control architecture and an algorithm which integrates cerebellar model articulation controller (CMAC) or fuzzified CMAC with fuzzy logic control and bang bang control under an intelligent supervisor.
The structure learning possesses the ability of on-line generation and elimination of layers to achieve optimal wavelet CMAC structure, and the parameter learning can adjust the interconnection weights of wavelet CMAC to achieve favorable approximation performance.
With the proposed approach, the task of fuzzy CMAC architecture determination by preliminary knowledge or trials can be freed when a well-organized and well-parameterized CMAC is represented to achieve desired approximation performance.
The paper simply introduces the CMAC model.
The other is the CMAC switching controller.
One is based on pure CMAC and another one based on both the CMAC and PID.
Adaptive CMAC is used to mimic an ideal controller and the compensation controller is designed to dispel the approximation error between adaptive CMAC and ideal controller.
The adsorption behavior of heavy metals on CMAC was analyzed with Elovich, intraparticle diffusion model, Lagergren first-order model.
Therefore, the total capacity for the dedicated channel scenario using (26) and (28) can be given as CTot = CMac + CFem.
Thus, with the proposed CMAC, a dynamic control approach is presented.
The developed TCNN provides more powerful representation than the traditional CMAC neural network.
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