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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Discrete-Time High Order Neural Control: Trained with Kalman Filtering

Discrete-Time High Order Neural Control: Trained with Kalman Filtering

Alanís, Alma Y.
Loukianov, Alexander G.
Sanchez, Edgar N.

Paperback

Series: Studies in Computational Intelligence, Book 112

Technology & EngineeringGeneral ComputersGeneral Mathematics

ISBN10: 3642096956
ISBN13: 9783642096952
Publisher: Springer Nature
Published: Nov 22 2010
Pages: 110
Weight: 0.39
Height: 0.25 Width: 6.14 Depth: 9.21
Language: English
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks, controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem, nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

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