Read e-book online 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 e-book is a part of a 3 quantity set that constitutes the refereed court cases 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 functions, robotics, facts mining and have extraction, chaos and synchronization, help vector machines, fault diagnosis/detection, image/video processing, and purposes 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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Such power 26 Y. -F. -J. Zhong allocation equilibrium strikes a balance between minimizing interference and maximizing rate. 5 Simulations and Discussion The terrain model we used is a 1000m×1000m square area with seven cells in it, on which 1000 MHs are pseudo-randomly moving along the dual ring topology. All the MHs are presented by {ui} i∈ [1, 2000], and all the links between MHs are bi-directional, presented by {vj} j ∈ [1,Y]. Each cell has a base station with omni-directional antenna at the center point and its radius is 250m.

A Model-Predictive Controller for Grain Drying. J. Food Engineering 49 (2001) 321-326 6. : Model-Based Control Strategies for Commercial Grain Drying Systems. Canadian Journal of Chemical Engineering 62 (1984) 773-779 7. : Fuzzy Logic Control for a Continuous Crossflow Grain Dryer. Food Process Engineering 16 (1993) 59-77 8. : Multivarable Control for an Industrial Rotary Dryer. Food Control 17 (2006) 653-659 9. : Investigation of Moisture Transfer Mechanism in Porous Media During a Rapid Drying Process.

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]. For training MPRFNN, the cost function is defined as follows: m m 1 1 2 2 JM (k) = (emi (k)) = (yˆi − Mi ) , (8) 2 i=1 2 i=1 where k is the training epoch, yˆi and Mi are, respectively, the output of MPRFNN for the drying process data of sample i and its moisture content, emi (k) is the error between yˆi and Mi at the training epoch k, and m is the number of the samples.

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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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