Neurofuzzy networks with nonlinear quantum learning

Authors:
Massimo Panella;Giuseppe Martinelli
Affiliations:
Department of Information and Communication, University of Rome "La Sapienza," Rome, Italy;Department of Information and Communication, University of Rome "La Sapienza," Rome, Italy
Venue:
IEEE Transactions on Fuzzy Systems
Year:
2009

Citing 12
Cited 3

Neurofuzzy adaptive modelling and control

Neurofuzzy adaptive modelling and control
A fast quantum mechanical algorithm for database search

STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A constructive approach to neuro-fuzzy networks

Signal Processing - Special issue on neural networks
Quantum artificial neural network architectures and components

Information Sciences—Informatics and Computer Science: An International Journal - Special Issue on Quantum Computing and Neural Information Processing
An introduction to quantum computing for non-physicists

ACM Computing Surveys (CSUR)
Neural Networks: A Comprehensive Foundation (3rd Edition)

Neural Networks: A Comprehensive Foundation (3rd Edition)
Quantum Genetic Optimization

IEEE Transactions on Evolutionary Computation
Guest Editorial: Fuzzy Logic Hardware Implementations

IEEE Transactions on Fuzzy Systems
An input-output clustering approach to the synthesis of ANFIS networks

IEEE Transactions on Fuzzy Systems
Quantum neural networks (QNNs): inherently fuzzy feedforward neural networks

IEEE Transactions on Neural Networks
An Experimental Study on Nonlinear Function Computation for Neural/Fuzzy Hardware Design

IEEE Transactions on Neural Networks

An affine fuzzy model with local and global interpretations

Applied Soft Computing
Classical and superposed learning for quantum weightless neural networks

Neurocomputing
A hierarchical procedure for the synthesis of ANFIS networks

Advances in Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

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.