Neural network architecture for control

Guez, A.; Eilbert, James L.; Kam, Moshe

doi:10.1109/37.1869

Cited by 112 publications

(21 citation statements)

References 11 publications

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“…In particular, dynamic networks such as the Hopfield and Cohen-Grossberg networks have been used within indirect and direct adaptive linear controllers, respectively [6,13]. Likewise, the Kohonen network has been applied successfully within an adaptive model-based control structure [7].…”

Section: 1 0 V E R V I E Wmentioning

confidence: 99%

Reliable neuro self-tuning control using autoassociative neural networks for the water treatment

Böhme

Fletcher

Cox

1999

Elektrotech. Inftech.

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Section: 1 0 V E R V I E Wmentioning

confidence: 99%

Reliable neuro self-tuning control using autoassociative neural networks for the water treatment

Böhme

Fletcher

Cox

1999

Elektrotech. Inftech.

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“…Here, the neural networks are trained with respect to previous movement pattern for learning optimized functions for predictions .The task of the neural networks in this application is to capture the unknown relation between the past and the future values of the movement pattern. This helps in predicting the future location of a mobile host for location management [13].…”

Section: A Neural Network Model For Location Updatementioning

confidence: 99%

Location Prediction of Mobility Management Using Soft Computing Techniques in Cellular Network

Parija¹,

Nanda²,

Sahu³

et al. 2013

IJCNIS

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-This work describes the neural network technique to solve location management problem. A multilayer neural model is designed to predict the future prediction of the subscriber based on the past predicted information of the subscriber. In this research work, a prediction based location management scheme is proposed for locating a mobile terminal in a communication without losing quality maintains a good response. There are various methods of location management schemes for prediction of the mobile user.

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“…Since the higher order subgrids probably have some nodes that are the same as the lower order subgrids, the set of the new possible centers provided by the th order subgrid is defined as and for (11) where is an empty set. It shows that the possible center set corresponding to the th subgrid does not include those that are given by the lower order subgrids, i.e., (12) For example, in the two-dimensional (2-D) case, let the edge length of rectangulars on the th subgrid be half of the th subgrid. The variable grid with three subgrids is shown in Fig.…”

Section: A Variable Gridmentioning

confidence: 99%

“…A large number of control structures have been proposed, including supervised control [55], direct inverse control [34], model reference control [39], internal model control [13], predictive control [14], [56], [29], gain scheduling [12], optimal decision control [10], adaptive linear control [7], reinforcement learning control [1], [3], variable structure control [30], indirect adaptive control [39], and direct adaptive control [19], [45], [50], [51]. The principal types of neural networks used for control problems are the multilayer perceptron (MLP) neural networks with sigmoidal units [34], [39], [48] and the radial basis function (RBF) neural networks [41], [43], [47].…”

Section: Introductionmentioning

confidence: 99%

Variable neural networks for adaptive control of nonlinear systems

Liu

Kadirkamanathan

Billings

1999

IEEE Trans. Syst., Man, Cybern. C

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Abstract-This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time according to specified design strategies so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBF's in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error. The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated examples.Index Terms-Adaptive control, neural networks, nonlinear systems, radial basis functions.

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Neural network architecture for control

Cited by 112 publications

References 11 publications

Reliable neuro self-tuning control using autoassociative neural networks for the water treatment

Reliable neuro self-tuning control using autoassociative neural networks for the water treatment

Location Prediction of Mobility Management Using Soft Computing Techniques in Cellular Network

Variable neural networks for adaptive control of nonlinear systems

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