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| Abstract |
Universal Learning Network(ULN), which can model and control large scale complicated systems naturally, consists of nonlinearly operated nodes and multi-branches that may have arbitrary time delays in...cluding zero or minus ones. It can therefore be applied to many kinds of systems which are difficult to be expressed by ordinary first order difference equations with one sampling time delay. In order to improve the generalization ability of ULN, in this paper an approach to optimize the structure of networks are presented, where not only parameters but also time delays in the ULN are adjusted. In the proposed method, both the compactness and error criterion function of the ULN are improved. Simulation results of a nonlinear system identification show that better performance can be obtained by the proposed method than the conventional method using only the parameter optimization.show more
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