AppART: An ART Hybrid Stable Learning Neural Network for Universal Function Approximation
Luis Martí, Alberto Policriti, Luciano García · 2002
This work describes AppART, an ART—based low parameterized neural model that incrementally approximates continuous—valued multidimensional functions from noisy data using biologically plausible processes. AppART performs a higher—order Nadaraya—Watson regression and can be interpreted as a fuzzy system. Some benchmark problems are solved in order to study AppART from an application point of view and to compare its results with the ones obtained from other models. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.