摘要
根据生物免疫系统抗体识别抗原机理,提出基于人工免疫算法的边坡稳定性预测模型。将边坡稳定状态训练样本集定义为抗原集,边坡稳定性影响因素定义为抗体、抗原上的基因段,通过反复进行遗传计算操作,构建对边坡稳定性具有很好表达能力的记忆抗体集合。计算预测样本与记忆抗体集合之间的亲和力,利用K-最近邻法预测样本的稳定状态。同时构建自适应人工免疫算法,可有效提高模型的有效性和可靠性。实例分析证实自适应人工免疫算法能有效预测边坡稳定状态,其预测精度优于基本人工免疫算法,表明自适应策略的有效性。该方法可避免直接建立边坡稳定性影响因素与稳定状态之间复杂非线性函数关系,能有效降低建模复杂度,具有更好的实用性。
Under the guidance of the simulation of antigen-antibody recognition in biological mechanisms of the immune system, based on the artificial immune algorithm, a prediction model for slope stability was introduced. The slope stability sample set was defined as antigen set, and the influence factors of slope stability were defined as the antibody. Through reiteration of genetic manipulation on the antigen gene segment, the antibody set that can perform slope stability well was developed. The affinity between prediction sample set and antibody set were calculated, and the KNN algorithm was used to predict the sample stability. The self-adaptive artificial immune algorithm was also employed to improve the model effectiveness and reliability. The case study confirms that self-adaptive artificial immune algorithm has a more accurate prediction than basic artificial immune algorithm, proving that the self-adaptive approach is effective. The new method can avoid from building complex ly reduce the modeling complexity and has better non-linear function between influence factors and stability, effectiveadaptability.
出处
《煤炭学报》
EI
CAS
CSCD
北大核心
2012年第6期911-917,共7页
Journal of China Coal Society
基金
浙江省自然科学基金资助项目(LY12D01008)
江西省自然科学基金资助项目(2010GZC0009)
江西省教育厅青年科学基金资助项目(GJJ11147)
关键词
人工免疫算法
边坡稳定性
预测模型
自适应
artificial immune algorithm
slope stability
prediction model
self-adaptive
