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• W295605916 abstract "A new method for function approximation and system identification based on the SelfOrganizing Map is presented. The standard Self-Organizing Map (SOM) is extended with Local Linear Mappings to enable the original algorithm to learn input-output relationships with reasonable accuracy. 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) of the input-output mapping that is calculated from the training pairs. A training algorithm for the Jacobian matrices is derived based on a Least Squares approximation by utilising properties of the pseudoinverses of vectors. The method is tested in function approximation of a multivariable function, system identification of a highly nonlinear system and system identification of a hydraulic actuator based on input-output measurements." @default.
• W295605916 created "2016-06-24" @default.
• W295605916 creator A5047144831 @default.
• W295605916 creator A5052916956 @default.
• W295605916 date "2007-01-01" @default.
• W295605916 modified "2023-09-25" @default.
• W295605916 title "Extended Self-Organizing Maps with Local Linear Mappings for Function Approximation and System Identification" @default.
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