An AI system for the decision to control parameters of TP film printing
Expert Systems with Applications: An International Journal
Neurofuzzy networks with nonlinear quantum learning
IEEE Transactions on Fuzzy Systems
Development of quantum-based adaptive neuro-fuzzy networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The estimations of ammonia concentration by using neural network SH-SAW sensors
Expert Systems with Applications: An International Journal
A new approach belonging to EDAs: quantum-inspired genetic algorithm with only one chromosome
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A quantum neural networks data fusion algorithm and its application for fault diagnosis
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
High-performance dynamic quantum clustering on graphics processors
Journal of Computational Physics
Quantized Neural Modeling: Hybrid Quantized Architecture in Elman Networks
Neural Processing Letters
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This paper introduces quantum neural networks (QNNs), a class of feedforward neural networks (FFNNs) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNNs can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNNs are capable of recognizing structures in data, a property that conventional FFNNs with sigmoidal hidden units lack