Naslov
Discrete time neural network synthesis using interaction activation functions

Autori
Novaković, Branko

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of SPIE"s International Symposium on Intelligent Systems and Advanced Manufacturing / Schenker, Paul S. - Boston (MA) : SPIE, 1996, 231-238

Skup
SPIE"s International Symposium on Intelligent Systems and Advanced Manufacturing

Mjesto i datum
Boston (MA), Sjedinjene Američke Države, 18.11.1996. - 22.11.1996

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
Recurrent neural networks; interaction activation functions; time-discrete domain synthesis; one-step learning; nonlinear dynamical systems; nonlinear robot control.

Sažetak
Abstract - A new very fast algorithm for synthesis of discrete-time neural networks (DTNN) is proposed. For this purpose the following concepts are employed: (i) introduction of interaction activation functions, (ii) time-varying DTNN weights distribution, (iii) time-discrete domain synthesis and (iiii) one-step learning iteration approach.. The proposed DTNN synthesis procedure is useful for applications to identification and control of nonlinear, very fast, dynamical systems. In this sense a DTNN for a nonlinear robot control is designed. As the contributions of the paper, the following items can be cited. A nonlinear, discrete-time state representation of a neural structure was proposed for one-step learning. Within the structure, interaction activation functions are introduced which can be combined with input and output activation functions. A new very fast algorithm for one step learning of DTNN is introduced, where interaction activation functions are employed. The functionality of the proposed DTNN structure was demonstrated with the numerical example where a DTNN model for a nonlinear robot control is designed. This DTNN model is trained to imitate a nonlinear robot control algorithm, based on the dynamics of the full robot model of RRTR-structure. The simulation results show the satisfactory performances of the trained DTNN model.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo

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