Stability analysis of nonlinear systems using high order derivatives of universal learning networks

K. Hirasawa; Y. Yu; J. Hu; J. Murata

Stability analysis of nonlinear systems using high order derivatives of universal learning networks

K. Hirasawa^*, Y. Yu, J. Hu, J. Murata

^*Corresponding author for this work

Research output: Contribution to conference › Paper › peer-review

Abstract

In this paper, a stability analysis method based on the higher order derivatives of ULNs is proposed. In the proposed method, the following are proposed. Firstly, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Secondly, the locally asymptotically stable region, where asymptotical stability is secured approximately is obtained by comparing the first order derivatives and higher order derivatives.

Original language	English
Pages	1273-1278
Number of pages	6
Publication status	Published - 2001 Jan 1
Externally published	Yes
Event	International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States Duration: 2001 Jul 15 → 2001 Jul 19

Conference

Conference	International Joint Conference on Neural Networks (IJCNN'01)
Country/Territory	United States
City	Washington, DC
Period	01/7/15 → 01/7/19

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

@conference{9f27f7fc70c84547b84968b9e670e232,

title = "Stability analysis of nonlinear systems using high order derivatives of universal learning networks",

abstract = "In this paper, a stability analysis method based on the higher order derivatives of ULNs is proposed. In the proposed method, the following are proposed. Firstly, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Secondly, the locally asymptotically stable region, where asymptotical stability is secured approximately is obtained by comparing the first order derivatives and higher order derivatives.",

author = "K. Hirasawa and Y. Yu and J. Hu and J. Murata",

year = "2001",

month = jan,

day = "1",

language = "English",

pages = "1273--1278",

note = "International Joint Conference on Neural Networks (IJCNN'01) ; Conference date: 15-07-2001 Through 19-07-2001",

}

TY - CONF

T1 - Stability analysis of nonlinear systems using high order derivatives of universal learning networks

AU - Hirasawa, K.

AU - Yu, Y.

AU - Hu, J.

AU - Murata, J.

PY - 2001/1/1

Y1 - 2001/1/1

N2 - In this paper, a stability analysis method based on the higher order derivatives of ULNs is proposed. In the proposed method, the following are proposed. Firstly, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Secondly, the locally asymptotically stable region, where asymptotical stability is secured approximately is obtained by comparing the first order derivatives and higher order derivatives.

AB - In this paper, a stability analysis method based on the higher order derivatives of ULNs is proposed. In the proposed method, the following are proposed. Firstly, if the absolute values of the first order derivatives of any nodes with respect to any initial disturbances approach zero at time infinity, then the trajectory is locally asymptotically stable. Secondly, the locally asymptotically stable region, where asymptotical stability is secured approximately is obtained by comparing the first order derivatives and higher order derivatives.

UR - http://www.scopus.com/inward/record.url?scp=0034863180&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0034863180&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:0034863180

SP - 1273

EP - 1278

T2 - International Joint Conference on Neural Networks (IJCNN'01)

Y2 - 15 July 2001 through 19 July 2001

ER -

Stability analysis of nonlinear systems using high order derivatives of universal learning networks

Abstract

Conference

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this