CAJ | 학술논문

为了研究Elman神经网络和标准BPNN中何种网络类型更适合于解决模式分类问题，分别构建了基于Elman神经网络的分类模型和基于标准BPNN的分类模型。以平面上二维向量模式的分类为例，对2种分类模型进行训练和泛化能力测试。仿真结果表明，在训练样本数量相等且中小规模网络的条件下，Elman网络模型比BP网络模型具有更高的分类精度，更快的收敛速度，更适合于解决模式分类问题。
위료연구Elman신경망락화표준BPNN중하충망락류형경괄합우해결모식분류문제，분별구건료기우Elman신경망락적분류모형화기우표준BPNN적분류모형。이평면상이유향량모식적분류위례，대2충분류모형진행훈련화범화능력측시。방진결과표명，재훈련양본수량상등차중소규모망락적조건하，Elman망락모형비BP망락모형구유경고적분류정도，경쾌적수렴속도，경괄합우해결모식분류문제。
To study which type of network in Elman neural networks or standard BPNN is more effective for pattern classifi-cation,two classification models based on Elman neural network and standard BPNN are established respectively. The classifica-tion of two- dimensional vector pattern on a plane is taken as an example to train the two classification models and test their generalization abilities respectively. The simulation results show that Elman neural network has higher classification accuracy and faster convergence speed than BPNN under the conditions of the same quantity of the training samples and small or medium size network. And this makes Elman neural network more suitable for solving the problem of pattern classification.