Innovations in ART Neural Networks
Beatrice Lazzerini, Ugur Halici
Springer Science & Business Media, Mar 6, 2000 - Computers - 258 pages
In the last two decades the artificial neural networks have been refined and widely used by the researchers and application engineers. We have not witnessed such a large degree of evolution in any other artificial neural network as in the Adaptive Resonance Theory (ART) neural network. The ART network remains plastic, or adaptive, in response to significant events and yet remains stable in response to irrelevant events. This stability-plasticity property is a great step towards realizing intelligent machines capable of autonomous learning in real time environment.
The main aim of this book is to report a very small sample of the research on the evolution of ART neural network and its applications. Interested readers may refer literature for many more innovations in ART such as Fuzzy ART, ART2, ART2-a, ARTMAP, ARTMAP-PI, ARTMAP-DS, Gaussian ARTMAP, EXACT ART, and ART-EMAP.
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active Adaptive Resonance Theory algorithm ART module ART network ART-based ART2 ARTa artificial neural networks associated attributes backpropagation Bayesian binary Carpenter Cascade ARTMAP category node category prototypes chip choice function circuit complement coding component computed current mirror current sources data set database denotes domain theory equation estimate F2 layer F2 node fast learning Froggit Fuzzy ART Grossberg hardware HART-S hierarchical ART hierarchical clustering IEEE implementation incremental input patterns input sample input vector landmine machine learning map field match criterion MCSs measured method mismatch modular NMOS number of categories number of samples operation optimisation output performance PFAM networks pixel PMOS prediction presented recognition categories reset rule sequence shown in Figure standard Fuzzy ARTMAP supervised learning synapses Table target classes total number training set training vector transistor TRISS updated vigilance level vigilance parameter weight rectangle weight vector