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
收藏
页码：151 / 156
页数：6
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