Nonlinear quantum processing allows the solution of an optimization problem by the exhaustive search on all its possible solutions. Hence, it can replace advantageously the algorithms for learning from a training set. In order to pursue this possibility in the case of neurofuzzy networks, we propose in this paper to tailor their architectures to the requirements of quantum processing. In particular, superposition is introduced to pursue parallelism and entanglement to associate the network performance with each solution present in the superposition. Two aspects of the proposed method are considered in detail: the binary structure of membership functions and fuzzy reasoning and the use of a particular nonlinear quantum algorithm for extracting the optimal neurofuzzy network by exhaustive search.
Neurofuzzy Networks With Nonlinear Quantum Learning / Panella, Massimo; Martinelli, Giuseppe. - In: IEEE TRANSACTIONS ON FUZZY SYSTEMS. - ISSN 1063-6706. - STAMPA. - 17:3(2009), pp. 698-710. [10.1109/tfuzz.2008.928603]
Neurofuzzy Networks With Nonlinear Quantum Learning
PANELLA, Massimo;MARTINELLI, Giuseppe
2009
Abstract
Nonlinear quantum processing allows the solution of an optimization problem by the exhaustive search on all its possible solutions. Hence, it can replace advantageously the algorithms for learning from a training set. In order to pursue this possibility in the case of neurofuzzy networks, we propose in this paper to tailor their architectures to the requirements of quantum processing. In particular, superposition is introduced to pursue parallelism and entanglement to associate the network performance with each solution present in the superposition. Two aspects of the proposed method are considered in detail: the binary structure of membership functions and fuzzy reasoning and the use of a particular nonlinear quantum algorithm for extracting the optimal neurofuzzy network by exhaustive search.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.