Title :
Fusion algorithm based on fuzzy neural networks
Author :
Natekin, Alexey ; Knoll, Aaron
Author_Institution :
fortiss Gmbh, Munich, Germany
Abstract :
The problem of optimal fusion of several predictive machine learning regression models is considered. The method of combining different predictive models based on additive fuzzy systems is presented. The framework of model fusion based on fuzzy neural networks is described and the appropriate algorithms are derived. Learning process justifications and the requirement of the separate fusion set are discussed. The presented models are supported with the real world application example of robotic hand control.
Keywords :
dexterous manipulators; fuzzy neural nets; fuzzy set theory; fuzzy systems; learning (artificial intelligence); optimal control; regression analysis; sensor fusion; additive fuzzy systems; fusion set; fuzzy neural networks; optimal fusion algorithm; predictive machine learning regression models; robotic hand control; Additives; Data models; Fuzzy neural networks; Joints; Predictive models; Training; data fusion; fuzzy associative memory; machine learning; model fusion; predictive ensembles;
Conference_Titel :
Intelligent Signal Processing (WISP), 2013 IEEE 8th International Symposium on
Conference_Location :
Funchal
Print_ISBN :
978-1-4673-4543-9
DOI :
10.1109/WISP.2013.6657480