Quantum wavelet neural networks and its application in breakout prediction
Peng Li · Computer Engineering and Applications Journal · 2008
In order to overcome the problems of slow speed and low accuracy of convergence and the shortcomings of generalization ability for pattern recognition of the traditional neural networks,the quantum neural network combines with wavelet theory form the wavelet quantum neural network model is given.The quantum neurons of hidden layer of the model using a linear superposition of wavelet function as incentive function,called multi-wavelet incentive function,such hidden layer neurons not only can express more of the status and magnitude,but also can improve network speed and accuracy of convergence.The same time this paper presents a learning algorithm.And the validity of the model and the study algorithm are proved by its application in recognition of breakout prediction.