Neural network based system identification of agricultural machinery

被引：0

作者：

Moshou, D ^{[1
]}

Clijmans, L ^{[1
]}

Anthonis, J ^{[1
]}

Kennes, P ^{[1
]}

Ramon, H ^{[1
]}

机构：

[1] Katholieke Univ Leuven, Mech Engn Lab, Dept Agroengn & Econ, B-3001 Heverlee, Belgium

来源：

MATHEMATICAL AND CONTROL APPLICATIONS IN AGRICULTURE AND HORTICULTURE | 1997年

关键词：

neural networks; self-organizing systems; identification algorithms; nonlinear systems; agriculture; machinery;

D O I：

暂无

中图分类号：

S8 [畜牧、动物医学、狩猎、蚕、蜂];

学科分类号：

0905 ;

摘要：

A new method for on-line system identification based on the Self-Organizing Map is presented. The standard Self-Organizing Map (SOM) is extended with Local Linear Mappings. To every node in the SOM along with the input weight two output weights are assigned: one that stores the output part of an input-output pair and one that stores the local gradient matrix (Jacobian) that is calculated from the training pairs. A training algorithm for the Jacobian matrices is derived. The method is tested in system identification of two Agricultural Machines: a flexible Spray Boom and a shaker with a nonlinear spring.

引用

页码：151 / 156

页数：6

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