Fuzzy ARTMAP
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An Fuzzy ARTMAP is a Supervised Neural Network that is based on Incremental Learning, Fuzzy Logic and Adaptive Resonance Theory methods.
- Context:
- It was first devolped by Carpenter et al. (1992),
- …
- Example(s):
- the standard Fuzzy Artmap neural network proposed by Carpenter et al. (1992),
- …
- Counter-Example(s):
- an IGNG,
- a Learn++,
- a TopoART,
- a Incremental SVM,
- a NGE System,
- a FMMC System.
- See: Artificial Neural Network, Fuzzy Set, Incremental Learning System, Machine Learning System, Classification System, Cumulative Learning System.
References
1992
- (Carpenter et al., 1992) ⇒ Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., & Rosen, D. B. (1992). "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps" (PDF). IEEE Transactions on neural networks, 3(5), 698-713.
- ABSTRACT: A neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors, which may represent fuzzy or crisp sets of features. The architecture, called fuzzy ARTMAP, achieves a synthesis of fuzzy logic and adaptive resonance theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Four classes of simulation illustrated fuzzy ARTMAP performance in relation to benchmark backpropagation and generic algorithm systems. These simulations include finding points inside versus outside a circle, learning to tell two spirals apart, incremental approximation of a piecewise-continuous function, and a letter recognition database. The fuzzy ARTMAP system is also compared with Salzberg's NGE systems and with Simpson's FMMC system.