Adaptive resonance theory

Neuronal networks are nowadays predominantly applied in classification tasks. Here, three kind of networks are tested First the backpropagation network is used, due to the fact that it is the most robust and common network. The other two networks which are considered within this study have special adapted architectures for classification tasks. The Learning Vector Quantization (LVQ) Network consists of a neuronal structure that represents the LVQ learning strategy. The Fuzzy Adaptive Resonance Theory (Fuzzy-ART) network is a sophisticated network with a very complex structure but a high performance on classification tasks. Overviews on this extensive subject are given in [2] and [6]. 

D. Wienke and L.M.C. Buydens, Adaptive resonance theory neural networks — the ART of real-time pattern recognition in chemical process monitoring Trends Anal. Chem., 99 (1995) 1-8. 

The Adaptive Resonance Theory (ART) family of approaches (described for process applications by Whiteley and Davis, 1994), although... 

Development of a GPC shape interpreter is characterized by a large number of training examples of normal shapes and a few training examples of each of the abnormal shapes. Because of the existence, but limited availability, of abnormal examples, Adaptive Resonance Theory (see Sec-... 

Whiteley, J. R.. and Davis, J. F., A similarity-based approach to interpretation of sensor data using adaptive resonance theory. Comput. Chem. Eng. 18(7), 637-661 (1994). 

Neural network learning algorithms BP = Back-Propagation Delta = Delta Rule QP = Quick-Propagation RP = Rprop ART = Adaptive Resonance Theory, CP = Counter-Propagation. 

Wang, X.Z. and Chen, B.H. (1998). Clustering of Infrared Spectra of Lubricating Base Oils Using Adaptive Resonance Theory. J.Chem.Inf.Comput.ScL, 38,457-462. 

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