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

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

This publication 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. assurance contains neural networks for keep watch over functions, robotics, info 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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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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The moisture controller is able to operate in conjunction with a grain quality controller. Jover and Alastruey [8] compared the multivariable and monovariable control scheme for an industrial rotary dryer and concluded that the settling time of the multivariable control scheme is shorter than that of the multivariable control scheme. However, to our knowledge, the online sensor-measured accuracy D. Liu et al. ): ISNN 2007, Part I, LNCS 4491, pp. 29–37, 2007. c Springer-Verlag Berlin Heidelberg 2007 30 C.

3. FNN control system Where y d is the reference speed, y is the output speed of USM, u is the control variable of the USM, and Δe is the change of e respectively. Firstly, define E = 1 / 2( y d − y ) 2 ≡ 1 / 2e 2 . Then we have the online learning algorithm of the FNNC ∂E ∂E ∂y ∂u ∂y ∂u = = −e , ∂a j ∂y ∂u ∂a j ∂u ∂a j (5) where ∂y ∂u can approximately be equal to Δy Δu . Then (5) can be written as ∂E Δy ≈ −e ∂a j Δu ∑ k ⎡ ∂u ∂xk4 ∂x 3j ⎤ Δy ⎢ 4 3 ⎥ = −e Δu ⎢⎣ ∂xk ∂x j ∂a j ⎥⎦ ⎡ ∑ ⎢⎢⎣w x k 3 k i ⎤ sgn( x 2j − a j )(c j − x 2j )⎥ , ∂a j ⎥⎦ ∂L j (6) In a similar way, we can obtain ∂E Δy ≈ −e ∂b j Δu ⎡ ∑ ⎢⎣⎢w x k 3 k i ⎤ sgn( x 2j − c j )(b j − x 2j )⎥ , ∂b j ⎦⎥ ∂R j (7) An Improved Fuzzy Neural Network for Ultrasonic Motors Control ∂E Δy ≈ −e ∂c j Δu ∑{w x 3 k i k ∂L j ∂c j [sgn(x 2j − a j )(c j − x 2j ) + ∂E Δy = −−e ∂wk Δu ∑x ∂R j ∂c j 4 k sgn( x 2j − c j )(b j − x 2j )]} , 11 (8) , (9) k where, ∂L j ∂a j , ∂R j ∂c j and ∂R j ∂b j can be obtained from (2) and (3).

The Flight Gear cockpit window Fig. 7. The Flight Gear Flight conditions and HUD (Head-Up Display) window Figure 5 depicts the block diagram of the interface between the Simulink and the Flight Gear, the inputs to the block being the aircraft states and the other information available as the outputs of the Aerosim block. UDP is used for communication between the two softwares. In Figs. 6 and 7, two snapshots of the Flight Gear windows are shown. Due to the unavailability of Aerosonde, T-38 TALON aircraft is visualized.

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