In further work on recognition and learning we present a reactor network consisting of four electrically coupled chemical reactors that are connected via Pt working electrodes in the fashion of a Hopfield network. Each reactor can assume either a periodic (P) or a nodal (N) state in the Belousov-Zhabotinsky (BZ) reaction. Two out of 16 (2~4) dynamical patterns are encoded by local coupling. The encoded patterns have been chosen such that their Hopfield matrix shows both positive and negative coupling strengths. To successfully recognize all remaining (14) patterns, an averaging procedure for all amplitudes was introduced. Numerical simulations using the seven-variable Gyorgyi-Field model for the BZ reaction are in good agreement with the recognition experiments. We also simulate an iterative learning method to build up the synaptic strengths from a random Hopfield matrix without any back-propagation of errors. Recognition occurs abruptly at a certain number of iterations in the absence of any noise reminiscent of a phase transition. The inclusion of parameter noise is found to always broaden the recognition probability. Parameter noise enhances the recognition of patterns in the early iteration stages, while the recognition probability is drastically reduced in the later stages of iterative learning.
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