In this paper we address a new type of elementary neurofuzzy unit called nullneuron. A nullneuron is a generalization of and/or neurons based on the concept of nullnorm, a category of fuzzy sets operators that generalizes triangular norms and conorms. The nullneuron model is parametrized by an element u, called the absorbing element. If the absorbing element u = 0, then the nullneuron becomes a and neuron and if u = 1, then the nullneuron becomes a dual or neuron. Also, we introduce a new learning scheme for hybrid neurofuzzy networks based on nullneurons using reinforcement learning. This learning scheme adjusts the weights associated with the individual inputs of the nullneurons, and learns the role of the nullneuron in the network (and or) by individually adjusting the parameter u of each nullneuron. Nullneuron-based neural networks and the associated learning scheme is more general than similar neurofuzzy networks because they allow different triangular norms in the same network structure. Experimental results show that nullneuron-based networks provide accurate results with low computational effort.
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