Abstract
Many cascade processes, such as wastewater treatment plant, include complex nonlinear sub-systems and many variables. The normal input-output relation only represent the first block and the last block of the cascade process. In order to model the whole process. We use hierarchical dynamic neural networks to identify the cascade process. The internal variables of the cascade process are estimated. Two stable learning algorithms and theoretical analysis are given. Real operational data of a wastewater treatment plant are applied to illustrate this new neural modeling approach.
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