摘要
针对资源分配网络算法鲁棒性较差,泛化能力不能保证等缺点,提出了一种将滑动数据窗口技术和梯度法相结合的资源优化网络,同时为避免网络过于庞杂,实现了隐节点的合并.在此基础上,建立了一种基于混杂神经网络的信用评估模型,并用该评估模型对上市公司的信用评级,表明了该评估模型的可行性和有效性.
To overcome disadvantages of Resource-Assigning Network algorithm such as bad robustness and weak generalization, Resource-Optimizing Network is proposed, which is combined sliding data window with grads. Meanwhile, to avoid numerous and jumbled network, this algorithm achieves combination of hidden neurons. On the basis, a credit rating model based on mixed neural network is proposed and applied to lots of listed companies. The result shows the feasibility and effectiveness of the evaluation model.
出处
《四川大学学报(自然科学版)》
CAS
CSCD
北大核心
2008年第4期785-789,共5页
Journal of Sichuan University(Natural Science Edition)
关键词
资源分配网络
资源优化网络
信用评估
滑动数据窗口
resource-assigning network, resource-optimizing network, credit rating, sliding data window