Abstract
In order to solve the difficulty in complicated system control, a new direct inverse model control strategy is proposed based on a new improved CMAC (Cerebellar Model Articulation Controller) neural network to control a kind of nonlinear system with strong hysteresis i.e. continuous-stirred tank reactor (CSTR). The idea of credit is introduced to help design a new Improved Credit Assigned CMAC (ICA-CMAC) with fast learning speed, which is helpful in real time control of CSTR. Simulation results show that the ICA-CMAC based method performs faster than conventional CMAC, and is strong in self-learning and helpful for improving the nonlinear control performance.
This work is supported by Zhejiang Nature Science Foundation under Grant Y1080776 and National Science Foundation, China under Grant 60702023.
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Ge, Y., Ma, S., Luo, X. (2010). Direct Inverse Model Control Based on a New Improved CMAC Neural Network. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_4
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DOI: https://doi.org/10.1007/978-3-642-14922-1_4
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