Download Advances in Neural Networks – ISNN 2015: 12th International by Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao PDF

By Xiaolin Hu, Yousheng Xia, Yunong Zhang, Dongbin Zhao

The quantity LNCS 9377 constitutes the refereed lawsuits of the twelfth overseas Symposium on Neural Networks, ISNN 2015, held in jeju, South Korea on October 2015. The fifty five revised complete papers awarded have been conscientiously reviewed and chosen from ninety seven submissions. those papers hide many issues of neural network-related study together with clever regulate, neurodynamic research, memristive neurodynamics, laptop imaginative and prescient, sign processing, computing device studying, and optimization.

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Read or Download Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings PDF

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Additional info for Advances in Neural Networks – ISNN 2015: 12th International Symposium on Neural Networks, ISNN 2015, Jeju, South Korea, October 15–18, 2015, Proceedings

Example text

1. Three-phase PWM voltage source rectifier. 1 NDSC for PWM Rectifier Controller Design The control objective is to make the output voltage track the reference output voltage. Meanwhile, the q axis input current must be forced to be zero to achieve unity power factor. The design procedure is elaborated in three steps as follows. Step 1: Define the first surface error as S1 = udc − udcr , (3) where udcr is the reference output voltage. The time derivative of S1 is given by 3Em S˙ 1 = id + δdc . 2C udc (4) Neural Dynamic Surface Control for PWM Rectifier 31 According to the Stone Weierstrass approximation theorem, there exists an ideal weight W1 such that δdc can be represented by an NN as δdc = W1T h(¯ udc ) + ε1 , (5) where h(·) is a known activation function; u ¯dc is the normalized value of udc ; ε1 is the function reconstruction error satisfying |ε1 | ≤ ε∗1 with ε∗1 being a positive constant; ||W1 || ≤ W1∗ with W1∗ being a positive constant.

Without solving any linear matrix inequality, the author investigated the adaptive synchronization of different Cohen-Grossberg chaotic neural networks [10]. Shi and Zhu studied the adaptive synchronization problem of Cohen–Grossberg neural networks [11]. However, most of recent work above on the synchronization of chaotic CohenGrossberg networks has been restricted to the less general synchronization scheme as © Springer International Publishing Switzerland 2015 X. Hu et al. ): ISNN 2015, LNCS 9377, pp.

2 Dynamic Model of the Rrectifier Under Study Fig. 1 represents the topology of three-phase PWM voltage source rectifier. ea , eb , ec are the supply power sources; ia , ib , ic are the input currents; ua , ub , uc are the rectifier input voltages; udc is the DC-link output voltage; L and R are the line inductance and resistance; C and RL are the DC-link capacitor and load-resistance. As the d axis is oriented in the direction of the supply voltage vector, the dynamic model of the rectifier under study can be described by 30 L.

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