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

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

ISBN-10: 354072382X

ISBN-13: 9783540723820

This publication is a part of a 3 quantity set that constitutes the refereed complaints of the 4th foreign Symposium on Neural Networks, ISNN 2007, held in Nanjing, China in June 2007. insurance contains neural networks for regulate purposes, robotics, facts mining and have extraction, chaos and synchronization, help vector machines, fault diagnosis/detection, image/video processing, and functions of neural networks.

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Read Online or Download Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part I PDF

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Additional 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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2. Structure of the predictive control of the dryer 34 C. Zhao et al. 1 (a). The outlet moisture content can be only measured offline by an oven box and the measured results will be delayed about two hours. Therefore, MPRFNN is trained offline and RPRFNN is intermittently trained online by BP algorithm. The samples of offline training MPRFNN are obtained artificially at the bottom of the dryer and their moisture contents are measured by oven box. The data of the temperature and the discharge rate can be calculated from the history records in the system [10].

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. The experimental results in the predictive control of a maize dryer with four stages show that the proposed scheme can meet the needs of commercial grain dryers.

An improved fuzzy neural network controller using NSSMF is constructed to control the speed of ultrasonic motors. A dynamic algorithm with adaptive learning rate is used to train FNNC online. The global convergence of the FNNC systems could be guaranteed by adjusting the adaptive learning rate. The validity of the proposed scheme is examined by simulated experiments. 1 Introduction An ultrasonic motor (USM) is a newly developed motor that has many excellent performances, and has been extensively used in many fields [1, 2].

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Advances in Neural Networks – ISNN 2007: 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part I by Shaocheng Tong, Yongming Li (auth.), Derong Liu, Shumin Fei, Zeng-Guang Hou, Huaguang Zhang, Changyin Sun (eds.)


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