摘要
该文利用复数BP学习算法,构造出量子神经元模型[1],并结合神经网络技术与量子理论,生成更有效的泛化和学习能力的量子神经网络。基于三层量子神经网络实现对谐波参数的检测,并以3次谐波和5次谐波为例,描述了该网络的训练流程和训练样本的构成。量子神经网络的实现采用Matlab进行编程,首先利用训练样本训练量子网络,之后检测构造的未训练样本数据集,通过仿真结果验证了该方法的可行性。该方法在谐波检测中具有较高的灵活性和精度,且对采样数目没有严格的限制,训练好的量子神经网络模型可用于谐波源固定的场合。
A quantum neuron model was developed by complex BP algorithm,and combine the quantum theory with neural network technology,a quantum neural network was proposed with more effective study and generalized ability.A method proposed to measure the parameters of harmonic is three lays quantum neural networks.With the example of 3rd and 5th harmonic parameters,elaborates the composition of the training method and training sample in the quantum neuron networks.A simulation which trains the quantum neutron network with training samples firstly,then measures untrained samples,is performed by Matlab programs.And the results of the simulation show the validity of the method.The proposed method has higher precision and flexibility in real time harmonic measuring and the proposed method has no restrict limitation to the samples number.The quantum neural network may suit for the occasion where the harmonic source is constant.
出处
《计算机与数字工程》
2012年第2期133-136,共4页
Computer & Digital Engineering
关键词
量子神经元
神经网络
谐波检测
a quantum neuron
netural network
harmonic measuring
