CAJ | 학술논문

研究神经网络的结构优化，提出采用基于贡献值与输出连接的权重来修剪节点，节点是直接剪枝而不是消除存有内在联系的节点；该方法认为神经元贡献值低于阈值，那么此神经元就是毫无意义的，同时将该算法应用于非线性函数逼近，实验结果表明，在不牺牲网络性能的情况下，采用新型剪枝算法来修剪神经网络节点是非常有意义的，所提出的算法也是非常有效的。
연구신경망락적결구우화，제출채용기우공헌치여수출련접적권중래수전절점，절점시직접전지이불시소제존유내재련계적절점；해방법인위신경원공헌치저우역치，나요차신경원취시호무의의적，동시장해산법응용우비선성함수핍근，실험결과표명，재불희생망락성능적정황하，채용신형전지산법래수전신경망락절점시비상유의의적，소제출적산법야시비상유효적。
In this paper ,based on the value and contribution of weights connected to the output node pruning ,pruning nodes are not directly linked to eliminate the inherent node ;neurons contribute to the method that is below the threshold value ,then the neuron is meaningless .Meanwhile ,the algo-rithm is applied to nonlinear function approximation ,the results show that the network performance without sacrificing the case ,using new pruning algorithm neural network node pruning is very mean-ingful that the proposed algorithm is also very effective .