The algorithm and application of quantum wavelet neural networks

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 quantum wavelet neural network model has been given. The hidden layer of the quantum wavelet neurons 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 simulation and application in pattern recognition for gearbox fault and continuous casting breakout prediction.