Identification and Control Optimization Algorithm Based on Neural Networks and Ant Colony

Abstract:

It is difficult to have good performance to control large delay time system. A neural network identification method for nonlinear system’s delay time was discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changed the input sample period of the neural network so that it could discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model prediction, searching PID controller parameters based on ant colony optimization algorithm, it was applied to control boiler combustion system. The simulation results show that this scheme has much better advantage of celerity and robustness.

• Periodical: Advanced Materials Research (Volumes 268-270)
• Pages: 1067-1072
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.268-270.1067
• Online since: July 2011
• Authors: Jia Yang, Qiang Xu, Cheng Bo Yu
• Keywords: Ant Colony Optimization Algorithm (ACOA), Identification of Delay Time, Large Delay Time System, Prediction Model

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