An adaptive neurocontroller with modified chaotic neural networks

Author

Kim, Sang-Hee ; Hong, Su-Dong ; Park, Won-Woo

Author_Institution

Sch. of Electron., Kumoh Nat. Univ. of Technol., Kyungbuk, South Korea

Volume

1

fYear

2001

fDate

2001

Firstpage

509

Abstract

This paper presents an indirect adaptive neurocontroller using modified chaotic neural networks (MCNN) for nonlinear dynamic system. A modified chaotic neural networks model is presented for simplifying the traditional chaotic neural networks and enforcing dynamic characteristics. A new dynamic backpropagation learning method is also developed. The proposed MCNN paradigm is applied to the system identification of a MIMO system and an indirect adaptive neurocontroller. The simulation results show the MCNN has robust adaptability to nonlinear dynamic systems

Keywords

MIMO systems; adaptive control; backpropagation; identification; neurocontrollers; nonlinear dynamical systems; MIMO system; adaptive control; backpropagation learning; chaotic neural networks; dynamic characteristics; identification; neurocontroller; nonlinear dynamic system; Biological system modeling; Chaos; MIMO; Neural networks; Neurofeedback; Neurons; Nonlinear dynamical systems; Recurrent neural networks; Robustness; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939072

Filename

939072