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Computer and Digital Engineering

Research on Quantum Neural Network and its Applications Based on Tanh Activation Function

Huifang Li

Published Jan 1, 2012• 6 citations
OpenAlex

Abstract

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.

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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.
Abstract Since the multi-level activation function quantum neural network (QNN) for pattern recognition was firstly proposed by Purushothaman and Karayiannis, more and more researches have been conducted on improving it. However, they all ignore that the QNN only uses multi-level activation function to simulate the concept of quantum superposition rather than really using quantum computing. In this paper, we propose a real QNN model based on multi-layer activation function. In addition, we present algorithms for updating weight parameters and quantum intervals, and also improve the learning algorithm for weight parameters using famous Levenberg–Marquardt algorithm. We also use the QNN for lie detection, and the simulation results of MATLAB prove that the performance of the model based on the corresponding algorithm are very strong.
The facial expression recognition is an important and complicated problem of pattern recognition field.An approach to facial expression recognition based on multi-level transfer function quantum neural networks(QNN)and multi-layer classifiers is presented in order to improve the recognition rate and recognition reliability.The QNN is trained and tested by the CMU face expression image database.The experiment results show that the method achieves excellent performance in terms of recognition rates and recognition reliability,and the superiority and potential of QNN in solving pattern recognition problems are abvious.
In this paper, a handwritten digital recognition system based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers is proposed. The recognition system proposed consists of two layer sub-classifiers, namely first-layer QNN coarse classifier and second-layer QNN numeral pairs classifier. Handwritten digital recognition experiments are performed by using data from MNIST database. Experiment results indicate the proposed QNN recognition system achieves excellent performance in terms of recognition rates and recognition reliability, and at the same time show the superiority and potential of QNN in solving pattern recognition problems.
The handwritten digital recognition is an important problem of pattern recognition field. An approach to handwritten digital recognition is presented based on multi-level transfer function quantum neural networks (QNN) and multi-layer classifiers. The QNN is trained and tested by the MNIST database. The experimental results indicate the method achieves excellent performance in terms of recognition rates and recognition reliability, and show the superiority and potential of QNN in solving pattern recognition problems.
The training algorithm and the structure of quantum neural networks (QNN) that based on multilevel activation function are presented in this paper. Aiming at the influence of the activation function of output layer and the phenomenon of error saturation in the training process on the output values and convergence property, a linear superposition of arctangent function is introduced in as hide layer activation function, and error saturation prevention (ESP) function is constructed to improve the convergence property of QNN. The results of simulation program show that the convergence property is improved obviously.
Inspired by the principle of superposing states in quantum mechanics,a new kind of feedforward neural network with single hidden layer was designed and named as Quantum Neural Networks.It has mainly been used for data classification.Multilevel activation functions is adopted in Quantum Neural Networks and a different objective function is used for the learning of quantum intervals of the multilevel hidden units;minimizing the class-conditional variances at the outputs of the hidden units.Quantum Neural Networks is endowed with a special ability of identifying uncertainty in data classification by its unique design.However,as different objective functions are used for the learning of synaptic weights and quantum intervals,conflict is observed in the iterative training process which will cause more iteration times for training and degrades the training results.Using the theory of Constrained Optimization,a new training algorithm is proposed in this paper by introducing penalty function into the steepest descent method.In the new training algorithm, the conflict is removed.Experimental results indicate that the new algorithm can increase the training speed and improve the performance of the trained network remarkably.