One of the central problems in the application of neural networks is finding the optimal network topology. This paper introduces the FAST architecture (flexible adaptable-size topology), an on-line, evolving neural network that dynamically adapts its topology through interactions with a problem-specific environment. We present a fully digital implementation of the network and demonstrate its viability on a pattern clustering task. We believe the FAST architecture holds potential by offering a fast, flexible platform for neural network applications.
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