Download Advances in Neural Networks – ISNN 2007: 4th International by Shaocheng Tong, Yongming Li (auth.), Derong Liu, Shumin Fei, PDF

By Shaocheng Tong, Yongming Li (auth.), Derong Liu, Shumin Fei, Zeng-Guang Hou, Huaguang Zhang, Changyin Sun (eds.)

This e-book is a part of a 3 quantity set that constitutes the refereed lawsuits of the 4th overseas Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. insurance contains neural networks for keep watch over purposes, robotics, facts mining and have extraction, chaos and synchronization, aid vector machines, fault diagnosis/detection, image/video processing, and functions of neural networks.

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Extra resources for Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part I

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The drying-rate parameter is updated intermittently according to the difference between the model-predicted and the sensor-measured outlet moisture contents. Zhang and Litchfield [7] investigated to use fuzzy control on a laboratory dryer for control of both the grain moisture content and breakage susceptibility; both the drying-air temperature and the discharge-Auger speed were adjusted. Liu and Bakker-Arkema [2,4,5] developed a distributed-parameter process model based on the fundamental laws of simultaneous heat and mass transfer and used it to establish a model predictive controller for dryer control.

04%(w. ). 5% (w. ) in China. 5%. Therefore, the control scheme proposed in this paper can meet the needs of commercial grain dryers. 6 Conclusion This paper proposes a model predictive control scheme of grain dryers by using the temperature and its variation based recurrent fuzzy neural network. This scheme overcomes the difficult control problem of grain dryers because of long delay, nonlinearity intrinsic to the grain drying process, and the lack of the online A Model Predictive Control of a Grain Dryer with Four Stages 37 sensor-measured accuracy of inlet and outlet moisture content.

However, PI and fuzzy type of altitude controllers have demonstrated superior performance. For some flight conditions, the ANFIS type controller has resulted in unstable performance. This has demonstrated that more stable learning algorithms need to be adopted. One possible solution could be the use of Variable Structure Systems theory based algorithms that are known for their stability [15]. References 1. , et al: Autopilot System for Kiteplane. IEEE-ASME Transactions on Mechatronics 11 (2006) 615-624 2.

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