【作者】 钮满春；

【导师】 刘若辰；

【作者基本信息】 西安电子科技大学 ， 电路与系统， 2010， 硕士

【摘要】 人工免疫网络算法是基于免疫学原理特别是独特型免疫网络原理提出的应用于智能计算领域的奠基性算法。然而目前不少人工免疫网络分类算法都或多或少的存在一些缺陷,其一是算法机制的问题:网络规模庞大、计算复杂、对抗原采用一次递呈,这些导致了算法运行效率低;其二是应用领域比较窄,多数集中于标准数据的分类与聚类。基于此,针对复杂数据分类与图像处理问题,本文分别提出了一种新的人工免疫网络分类算法、基于自适应PSO的人工免疫网络分类算法以及基于人工免疫网络的关联规则挖掘算法,具体研究工作如下:1.提出了一种新的人工免疫网络分类算法。该算法利用每个类别对应单个B细胞的策略,简化网络规模并减少了同类别B细胞之间的抑制操作,同时引入了新的基于对训练样本正确识别率的亲合度评价函数,实现了基于抗原的优先级的选择策略。实验结果表明,该算法在分类精度上具有一定的优势,鲁棒性更好。2.提出了基于自适应PSO的人工免疫网络分类算法。该方法将自适应权值的PSO算法引入到人工免疫网络算法的变异算子中,同时采用各个B细胞包含抗原(即训练样本)所有类别的信息的策略。使得该算法具有良好的全局搜索能力以及快速的收敛速度。3.提出了基于人工免疫网络的关联规则挖掘算法。该方法将数据挖掘中的关联规则引入人工免疫网络算法中,用寻找最优关联规则来替代最佳聚类中心。对比实验表明,该方法在处理多类别以及名词性(nominal)特征数据时具有令人满意的分类精度和收敛速度。更多还原

【Abstract】 Artificial Immune Network (AIN) algorithm is the foundational algorithm which is based on Immune theory and especial the Idiotypic Immune Network (IIN) theory. And AIN has been applied into the field of intelligent computing. However, there are some shortcomings in the most existing AIN classification algorithms. On the one hand, the shortcomings lie in the mechanism of algorithm such as large-scale network, complex computation, and only once presenting the antigens, which resulted in the problem of low efficiency. On the other hand, they lie in the narrow applications, most AIN algorithms focus on the standard datasets classification and clustering.After analyzing above problems and aim to solve complex data classification and image classification problems, three AIN classification algorithms are proposed in this paper, the main work can be outlined as follows:(1) A new Artificial Immune Network classification algorithm is proposed. In the proposed algorithm, only one B-cell is used to denote single class in order to reduce the scale of network, and avoid the suppression operation between B-cells, moreover, a new affinity function based on the correct rate is proposed to realize the evaluation strategy based on antigen priority. The results of experiments indicate that the new algorithm has better accuracy and robustness.(2) A self-adaptive PSO based Artificial Immune Network classification algorithm is proposed. This method applies the self-adaptive PSO into the mutation process of the artificial immune network algorithm. Moreover, the strategy of every B-cell containing all types of information for antigens is used in the proposed algorithm. So the new algorithm has good global search ability and fast convergence speed.(3) An associate rules mining algorithm based on artificial immune network is proposed. This method introduces the association rules used in the data mining into the Artificial Immune Network algorithm. And it uses researching the optimal association rules to replace finding the best cluster center. Comparative experiments show that the method has satisfactory classification accuracy and convergence speed in dealing with multi-class and nominal attributes data.更多还原