The parameters in large scale neural networks can be divided into two classes. One class is necessary for a certain purpose while another class is not directly needed. The parameters in the latter are defined as superfluous parameters. How to use these superfluous parameters effectively is an interesting subject. In this paper, it is studied how the generalization ability of dynamic systems can be improved by use of network's superfluous parameters. And a calculation technique is proposed which use second order derivatives of the criterion function with respect to superfluous parameters. So as to investigate the effectiveness of the proposed method, simulations of modeling a nonlinear robot dynamics system is studied. Simulation results show that the proposed method is useful for improving the generalization ability of neural networks, which may model nonlinear dynamic systems.
|Journal||Proceedings of the IEEE International Conference on Systems, Man and Cybernetics|
|Publication status||Published - Dec 1 1999|
|Event||1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn|
Duration: Oct 12 1999 → Oct 15 1999
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Hardware and Architecture