摘要
为有效提高神经网络的分类性能,首先提出了一个可处理模糊输入的模糊神经网络结构,然后由模糊输出和非模糊目标输出定义了代价函数,推导出相应的学习算法,并对该模糊神经网络进行反转,提出了模糊化的反转算法.最后,通过计算机仿真实际的模式分类问题,验证了所提出的方法的有效性.实验结果表明,所提出的方法具有学习效率高、分类准确率高、泛化能力高的优点.
In order to effectively improve the classification performance of neural network, first architecture of fuzzy neural network with fuzzy input was proposed. Next a cost function of fuzzy outputs and non-fuzzy targets was defined. Then a learning algorithm from the cost function for adjusting weights was derived. And then the fuzzy neural network was inversed and fuzzified inversion algorithm was proposed. Finally, computer simulations on real-world pattern classification problems examine the effectives of the proposed approach. The experiment results show that the proposed approach has the merits of high learning efficiency, high classification accuracy and high generalization capability.
出处
《红外与毫米波学报》
SCIE
EI
CAS
CSCD
北大核心
2005年第1期15-18,共4页
Journal of Infrared and Millimeter Waves
基金
国家自然科学基金资助项目(60135010)