Fast convergence with low precision weights in ART1 networks

A new learning law, the Direct Coding Rule, is proposed for bottom-up long term memory learning in Adaptive Resonance Theory (ART) networks. This law requires less computational precision than the traditional Weber Law Rule and modifies the search dynamics of the network to accelerate convergence. Following a brief mathematical analysis of the new learning law, an ART1 network based on this law is applied to a passive radar detection problem. The simulation results allow comparison of the new law to the Weber Law Rule, with and without weight quantization, from the speed and cost viewpoints.<
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