Do we really need multiplier-based synapses for neuro-fuzzy classifiers?

The purpose of this paper is to show that the standard, multiplier-based synapse, may be replaced by a more convenient to implement synaptic model, while maintaining the overall classification performances of a neuro-fuzzy network. The new synaptic model was called a "comparative synapse" since computation is based mainly on comparisons. The incremental learning rule derived for the new synaptic model has also implementation advantages over the learning rule used by the multiplier-based synapses. Classification performances were investigated for different problems when both synaptic models (multiplier-based and comparative) were employed, showing very small dependence of the overall neural network system performance on the choice of the synaptic model.