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Original scientific paper

Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems

Dubravko Majetić ; Faculty of Mechanical Engineering and Naval Architecture, University of Zagreb, Zagreb, Croatia

Fulltext: english, pdf (4 MB) pages 99-106 downloads: 112* cite
APA 6th Edition
Majetić, D. (1995). Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems. Journal of computing and information technology, 3 (2), 99-106. Retrieved from https://hrcak.srce.hr/150431
MLA 8th Edition
Majetić, Dubravko. "Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems." Journal of computing and information technology, vol. 3, no. 2, 1995, pp. 99-106. https://hrcak.srce.hr/150431. Accessed 10 Jul. 2020.
Chicago 17th Edition
Majetić, Dubravko. "Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems." Journal of computing and information technology 3, no. 2 (1995): 99-106. https://hrcak.srce.hr/150431
Harvard
Majetić, D. (1995). 'Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems', Journal of computing and information technology, 3(2), pp. 99-106. Available at: https://hrcak.srce.hr/150431 (Accessed 10 July 2020)
Vancouver
Majetić D. Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems. Journal of computing and information technology [Internet]. 1995 [cited 2020 July 10];3(2):99-106. Available from: https://hrcak.srce.hr/150431
IEEE
D. Majetić, "Dynamic Neural Network for Prediction and Identification of Nonlinear Dynamic Systems", Journal of computing and information technology, vol.3, no. 2, pp. 99-106, 1995. [Online]. Available: https://hrcak.srce.hr/150431. [Accessed: 10 July 2020]

Abstracts
An attempt has been made to establish a nonlinear dynamic discrete-time neuron model, the so called Dynamic Elementary Processor (DEP). This dynamic neuron disposes of local memory, in that it has dynamic states. Based on the DEP neuron, a Dynamic Multi Layer Perceptron Neural Network is proposed to predict a time series of nonlinear chaotic system. As an another application of the proposed Dynamic Neural Network (DNN), the identification of a dynamic discrete-time nonlinear system whose measurement data are spoiled with noise is performed. To accelerate the convergence of proposed extended dynamic error back propagation learning algorithm, the momentum method is applied. The learning results are presented in terms that are insensitive to the learning data range and allow easy comparison with other learning algorithms, independent of machine architecture or simulator implementation.

Keywords
discrete dynamic neuron model; dynamic error-back propagation; nonlinear signal processing; chaotic system prediction; nonlinear system identification

Hrčak ID: 150431

URI
https://hrcak.srce.hr/150431

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