### Abstract

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.

Original language | English |
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Title of host publication | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |

Publisher | IEEE |

Volume | 5 |

Publication status | Published - 1999 |

Externally published | Yes |

Event | 1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' - Tokyo, Jpn Duration: 1999 Oct 12 → 1999 Oct 15 |

### Other

Other | 1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics' |
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City | Tokyo, Jpn |

Period | 99/10/12 → 99/10/15 |

### Fingerprint

### ASJC Scopus subject areas

- Hardware and Architecture
- Control and Systems Engineering

### Cite this

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics*(Vol. 5). IEEE.

**Improving generalization ability of universal learning networks with superfluous parameters.** / Han, Min; Hirasawa, Kotaro; Furuzuki, Takayuki; Murata, Junichi; Jin, Chun zhi.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*Proceedings of the IEEE International Conference on Systems, Man and Cybernetics.*vol. 5, IEEE, 1999 IEEE International Conference on Systems, Man, and Cybernetics 'Human Communication and Cybernetics', Tokyo, Jpn, 99/10/12.

}

TY - GEN

T1 - Improving generalization ability of universal learning networks with superfluous parameters

AU - Han, Min

AU - Hirasawa, Kotaro

AU - Furuzuki, Takayuki

AU - Murata, Junichi

AU - Jin, Chun zhi

PY - 1999

Y1 - 1999

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=0033329404&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0033329404&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0033329404

VL - 5

BT - Proceedings of the IEEE International Conference on Systems, Man and Cybernetics

PB - IEEE

ER -