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30 October 1996 Discrete time neural network synthesis using interaction activation functions
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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) timevarying DTNN weights distribution, (iii) time-discrete domain synthesis and (iiii) one-step learning iteration approach. . Theproposed DTNN synthesis procedure is useftil 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 ofthe 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 fimctionality 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 fill robot model of RRTR-structure. The simulation results show the satisfactoiy performances ofthe trained DTNN model.
© (1996) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Branko Novakovic "Discrete time neural network synthesis using interaction activation functions", Proc. SPIE 2905, Sensor Fusion and Distributed Robotic Agents, (30 October 1996); https://doi.org/10.1117/12.256336
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