基于人工免疫网络的模式识别算法  

Pattern Recognizing Algorithm Based on Artificial Immune Network

作　　者：邓九英[1] 毛宗源[1] 罗英辉[2] 

机构地区：[1]华南理工大学自动化科学与工程学院,广东广州510640 [2]广东教育学院计算机科学系,广东广州510303

出　　处：《华南理工大学学报（自然科学版）》2008年第1期99-104,共6页Journal of South China University of Technology(Natural Science Edition)

基　　金：广东省科技攻关项目(2005B10201006)

摘　　要：针对实际对象数学模型不明确而难以控制的问题,采用人工免疫网络的离散模型与学习算法,将人工免疫系统与神经网络结构的优势相结合,提出了一种基于人工免疫网络的模式识别算法,构造了对象识别的人工免疫网络模型.该算法综合了网络节点的定位与参数调整以及对基函数的平滑因子实施调谐等功能,有效地解决了径向基函数(RBF)神经网络模式识别的两个阶段任务,使模式识别的精度有较大的改进.采用两个不同对象函数进行的仿真试验表明,该算法具有快速收敛性与较高的准确性.The actually complicated objects are difficult to control due to their unknown mathematical models. In order to overcome this difficulty, a pattern recognition algorithm based on the artificial immune network is proposed, in which the discrete models and learning algorithms of artificial immune network are adopted and the functions of artificial immune system are combined with the framework of artificial neural network. After that, an objectmodel based on artificial immune network is constructed. The proposed algorithm merges the positioning and para- meter adjustment of network nodes as well as the parameter tuning of basis functions, etc. Thus, the two stage tasks of Radial Basis Function （RBF） neural network are effectively accomplished and the recognition accuracy is greatly improved. Simulated results in terms of two object functions finally confirm the high convergence speed and great accuracy of the proposed algorithm.

关 键 词：人工免疫网络 模式识别 网络结构 参数优化 节点参数调整 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]