Development of a model-based dynamic recurrent neural network for modeling nonlinear systems

被引：0

作者：

Karam, Marc ^{[1
]}

Zohdy, Mohamed A. ^{[2
]}

机构：

[1] Tuskegee Univ, Dept Elect Engn, Tuskegee, AL 36088 USA

[2] Oakland Univ, Elect & Syst Engn Dept, Rochester, MI 48309 USA

来源：

INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, PROCEEDINGS | 2007年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this study we develop the theory lying behind a model-based dynamic recurrent neural network (ABDRAW) that has been previously used to improve the linearized models of nonlinear systems. The initial structure of the ABDRNN is based on the linearized system model. Afterwards, the ABDRAW is trained to represent the system's nonlinearities by adapting the weights of its nodes' activation functions using Back-Propagation. The MBDRNN is applied with analytical detail to an arbitrarily chosen Single-Input/Single-Output (SISO) second order nonlinear system, and comparisons are made between the linearized and MBDRNN models, showing that the MBDRRN effectively improved the linearized model.

引用

页码：503 / +

页数：2

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