Option pricing model calibration using a real-valued quantum-inspired evolutionary algorithm
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Quantum-Inspired Evolutionary Algorithms for Calibration of the VG Option Pricing Model
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
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In this paper, the signal recognition by using quantum neural network (QNN) is studied and simulated. The signals with fuzziness distributed in the boundary of two different types of signals could be effectively recognized due to the structure of QNN's hidden units. To demonstrate the capability of QNN in recognition, the signals in a two- dimension (NC2) non-convex system is simulated. All the experiments are also performed by using the traditional neural network (NN) for a comparison.