Neural network adaptive resonance theory

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. 

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

Wienke, D., Vandenbroek, W., Meissen, W., Buydens, L., Feldhoff, R., Kantimm, T., Huthfehre, T., Quick, L., Winter, F. Cammann, K. (1995) Comparison of an adaptive resonance theory-based neural network (ART- 2A) against other classifiers for rapid sorting of post consumer plastics by remote near-infrared spectroscopic sensing using an InGaAs diode array. Analytica ChimicaActa 317, 1-16. 

D. Wienke and L. Buydens, Trends AnaL Chem., 14, 398 (1995). Adaptive Resonance Theory Based Neural Networks—the ART of Real-Time Pattern Recognition in Chemical Process Monitoring ... 

Wienke, D., et al.. Comparison of an Adaptive Resonance Theory Based on Neural Network (ART-2a) against other Classifiers for Rapid Sorting of Post Consumer Plastics by Remote Near Infrared Spectroscopic Sensing Using an InGaAs Diode Array. AnaZ. Chim. Acta, 1995. 317 1-16. 

Domine and co-workers utilized the family of Adaptive Resonance Theory (ART and ART 2-A) based artificial neural networks for unsupervised and supervised pattern recognition (142,143). The simplest ART network is a vec-... 

More than 50 different types of neural network exist. Certain networks are more efficient in optimization others perform better in data modeling and so forth. According to Basheer (2000) the most popular neural networks today are the Hopfield networks, the Adaptive Resonance Theory (ART) networks, the Kohonen networks, the counter propagation networks, the Radial Basis Function (RBF) networks, the backpropagation networks and recurrent networks. 

Artificial neural networks (ANNs) are good at classifying non-linearly separable data. There are at least 30 different types of ANNs, including multilayer perceptron, radial basis functions, self-organizing maps, adaptive resonance theory networks and time-delay neural netwoiks. Indeed, the majority of ATI applications discussed later employ ANNs - most commonly, MLP (multilayer perceptron), RBF (radial basis function) or SOM (self-organizing map). A detailed treatise of neural networks for ATI is beyond the scope of this chapter and the reader is referred to the excellent introduction to ANNs in Haykin (1994) and neural networks applied to pattern recognition in Looney (1997) and Bishop (2(X)0). Classifiers for practical ATI systems are also described in other chapters of this volume. 

A second variation on this theme was used in a processor based on the adaptive resonance theory [74]. This system was essentially an SVMM with a smart pixel array as the central weighting mask. The functionality of the smart pixel was basically the same as that for the pixel in Fig. 62. The only difference was that a pre-determined weight mask was loaded and stored on the smart pixel array. The array was then turned on and the photodetectors were illuminated with the input light. If the light was present at the photodetector and the corresponding pixel mask was selected, then the pixel modulator was turned off. This functionality provides a powerful technique for fast processing and learning in a neural network. 

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