LAUSR

In this paper, a new efficient model of neural network is proposed, which is realized by the combination of two recent and successful neurocomputing paradigms. The idea behind the work is to realize a neural network constituted by multi-valued complex neurons, which are trained with the principles of extreme learning machine (ELM). The specific kind of used neuron allows a very straightforward derivation of the ELM, with no substantial modification in the weight adjustment procedure. The main advantages that clearly emerge by this model are represented by the further increasing of generalization performances in presence of noise, together with a generalized reduction of needed nodes (neurons) in the hidden layer. The effectiveness of the proposed model will be shown by some benchmark results, also compared with the original techniques.