Download Advances in Neural Networks – ISNN 2013: 10th International by Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang PDF

By Qinglai Wei, Derong Liu (auth.), Chengan Guo, Zeng-Guang Hou, Zhigang Zeng (eds.)

The two-volume set LNCS 7951 and 7952 constitutes the refereed lawsuits of the tenth overseas Symposium on Neural Networks, ISNN 2013, held in Dalian, China, in July 2013. The 157 revised complete papers offered have been conscientiously reviewed and chosen from a variety of submissions. The papers are prepared in following subject matters: computational neuroscience, cognitive technology, neural community types, studying algorithms, balance and convergence research, kernel tools, huge margin equipment and SVM, optimization algorithms, varational tools, keep an eye on, robotics, bioinformatics and biomedical engineering, brain-like platforms and brain-computer interfaces, facts mining and information discovery and different purposes of neural networks.

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Additional info for Advances in Neural Networks – ISNN 2013: 10th International Symposium on Neural Networks, Dalian, China, July 4-6, 2013, Proceedings, Part II

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Acknowledgements. This work was partially supported by Natural Science Foundation of China(NSFC) under grant #61273054 and #60975072, National Key Basic Research Program of China under grant #2013CB035503, Program for New Century Excellent Talents in University of China under grant #NCET-10-0021, Aeronautical Foundation of China under grant #20115151019, and YUYUAN Science and Technology Innovation Foundation for Undergraduate of Beihang University. References 1. : Implementation of Autonomous Visual Tracking and Landing for a Low-cost Quadrotor.

Transform from torque to pneumatic tension M = Fr (3) 4. Transform from pneumatic tension to the tilt angle of the quadrotor  d 2θ M J = m  dt   M = 2 Xl  1  J m = m1l 2 + 2m2l 2 3  (4) From 1, 2, 3, 4 we can get the following result through Laplace transformation. s + 311330 s 3 + 100s 2 (5) The PID controller architecture is shown in Fig. 2 [1]. The input of the controller is the errors, which can be obtained by our proposed chaotic EDA. The output of the controller is: ∞ u = k p e(t ) + ki  e(t )dt + kd 0 de(t ) dt (6) Considering there is no apparent relationship between the inputs and the outputs of the flight control system and in order to avoid the overshoot, we choose the following objective function [12].

Pattern recognition has been studied in the control literature in the 1960s together with adaptive, learning and self-organizing systems, see for instance[1]. In that time, a pattern in control was defined as a control situation which was represented by a set of state variables. Information of a control situation learned during the process of closed-loop control was taken as a control experience. Pattern recognition techniques were proposed to classify different control situations. Based on the classification result, an experienced controller corresponding to the specific control situation was selected to control the system[2].

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