基于MISO折线模糊神经网络的优化算法  

Optimization algorithms for MISO polygonal fuzzy neural networks

作　　者：王贵君[1] 何英 李晓萍[1] 

机构地区：[1]天津师范大学数学科学学院,天津300387 [2]湖北省恩施市第二中学,恩施445000

出　　处：《中国科学：信息科学》2015年第5期650-667,共18页Scientia Sinica(Informationis)

基　　金：国家自然科学基金项目(批准号:61374009)资助

摘　　要：基于折线模糊数及其扩展运算提出了一种新的网络——多输入单输出(MISO)折线模糊神经网络,并在转移函数满足非负连续单调情况下对该网络进行了一致性分析.此外,为避免误差函数关于连接权和阈值求偏导数的复杂计算过程,利用Hebb规则和粒子群算法分别设计了MISO折线模糊神经网络的Hebb算法和粒子算法.最后,通过模拟实例对两种优化算法进行了系统分析和比较.结果显示,基于MISO折线模糊神经网络的两种优化算法虽然具有一定随机性和参数多样性,但Hebb算法直观简单且容易实现,而粒子算法稳定性好且收敛速度较快.In this paper, we proposed a novel multi-input and single-output（MISO） polygonal fuzzy neural network based on polygonal fuzzy numbers and their extension operations. A uniformity analysis of this network is provided for cases where an activation function is nonnegative continuous and monotonous. In order to avoid the complex calculation of the partial derivatives of error functions related to connection weights and threshold values, we use the Hebb rule and the particle swarm algorithm to design two kinds of optimal learning algorithms to handle these two calculations, respectively. Using a simulation example, we analyzed and compared the two kinds of optimization algorithms in detail. The results showed that our optimization algorithms based on MISO fuzzy neural networks exhibited randomness and parameters diversity, but the Hebb algorithm was simple and easy to realize. The particle algorithm was stable and converged quickly.

关 键 词：折线模糊数 扩展运算 折线模糊神经网络 Hebb算法 粒子算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]