Comparison of the response of a time delay neural network with an analytic model

Author

Johnson, S.R.

Volume

2

fYear

2002

fDate

6/24/1905 12:00:00 AM

Firstpage

1120

Lastpage

1125

Abstract

Time-delayed neural networks (TDNNs) can be used to learn the dynamics of an unknown system from input-output data. In many cases a model of the system is also available in the form of a system of ODES, derived either from first principles or using heuristic arguments. In such cases a functional comparison can be made between the dynamic behaviour of the model with that of the trained TDNN for arbitrary inputs. We show that Volterra kernels for both the model system and the TDNN can be obtained, and thus the system responses compared, in a manner that is independent of the input. The techniques of structural bilinearisation of ODES and Volterra series expansion of a TDNN, are demonstrated by application to the Hodgkin-Huxley set of equations

Keywords

Volterra equations; delays; differential equations; modelling; neural nets; uncertain systems; Hodgkin-Huxley equation set; I/O data; ODE; TDNN; Volterra kernels; Volterra series expansion; analytic model; dynamics learning; heuristic arguments; input-output data; structural bilinearisation; time delay neural network response; unknown system; Delay effects; Delay systems; Equations; Kernel; Neural networks; Nonlinear dynamical systems; Nonlinear systems; Probes; Q measurement; Zinc;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on

Conference_Location

Honolulu, HI

ISSN

1098-7576

Print_ISBN

0-7803-7278-6

Type

conf

DOI

10.1109/IJCNN.2002.1007651

Filename

1007651