Abstract
A temperature control solution is proposed in this paper, which uses a self-tuning PID controller based on flexible neural network (FNN). The learning algorithm of FNN can adjust not only the connection weights but also the sigmoid function parameters. This makes FNN characterized with online learning and high learning speed. The FNN has the following advantages when applied to temperature control problems: high learning ability, which considerably reduces the controller training time; the mathematical model of the plant is not required, which eases the design process; high control performance. These advantages are verified by its application to a practical temperature controlled box, which is used in medicinal inspection. The proposed system presents better behavior than that when using traditional back-propagation neural network.
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© 2007 Springer-Verlag Berlin Heidelberg
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Chen, L., Ge, B., de Almeida, A.T. (2007). Self-tuning PID Temperature Controller Based on Flexible Neural Network. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_18
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DOI: https://doi.org/10.1007/978-3-540-72383-7_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
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