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- Recurrent neural networks have proven to be feasible in identification and control applications due to their flexible architecture and robustness. In this work, high-order neural network structures for a wastewater aerobic digestion process with organic compounds reduction and an anaerobic digestion process with biofuel production are proposed.
- Keywords: Neural network, PID control, margin stability 1. Introduction: Recently, PIDNN controller is one of the popular methods used for control complexes systems. Several robust and auto tuning techniques have been proposed in order to further improve the control and robust performance of the PIDNN controller 1,2,3,4.
Neural Network Pid Controller Auto-tuning Design And Application Online
Neural Network Pid Controller Auto-tuning Design And Application Center
May 27, 2013 Neural network PID controller auto-tuning design and application Abstract: The simple PID controller can't get the satisfied degree-especially for the time-varying objects and non-linear systems-the traditional PID controllers can do nothing for them to non-linear systems-the NN PID controller has a good controller effect in the non-line. That the auto-tuning of PID controller based on a neural network approach reduced the time duration necessary for auto-tuning in order of magnitude with respect to traditional methods. Mohammed Hassan and Ganesh Kothapalli 16 made a comparison between Neural Network based PI controller and Neural Network based PID controller using a pneumatic. Home Browse by Title Periodicals Neurocomputing Vol. C A novel algorithm for wavelet neural networks with application to enhanced PID controller design research-article A novel algorithm for wavelet neural networks with application to enhanced PID controller design.