Research on Quantum Neural Network and its Applications Based on Tanh Activation Function
Huifang Li · Computer and Digital Engineering · 2012
In this paper an improved quantum neural network(QNN) is presented based on multilevel activation function to solve the problems of precision inadequacy and low convergence rate of the BP neural network used in character recognition.Firstly,momentum term is used to updating the weights to accelerate the convergence rate of learning algorithm in QNN.Secondly,a linear superposition of hyperbolic tangent function is used as activation function of hidden unit in the new networks to classify pattern recognition problems that have uncertainty and overlapping data between two patterns.Finally,The experimental results for number,letter,and Chinese character recognition is provided and compared with both BP neural networks and the original Qnn.Results indicate that the improved Qnn not only takes great recognition rate,but also decreases the number of the training time comparing with the original one.The superiority is demonstrated by simulation.