本文選題：人工免疫網(wǎng)絡(luò)　切入點(diǎn)：多智能體技術(shù)　出處：《計(jì)算機(jī)應(yīng)用研究》2017年01期

【摘要】：在傳統(tǒng)的人工免疫網(wǎng)絡(luò)基礎(chǔ)上,將多智能體技術(shù)的典型策略融入到免疫網(wǎng)絡(luò)的進(jìn)化過程中。算法引入了鄰域克隆選擇,操作過程從局部到整體,能夠更加全面地模擬免疫網(wǎng)絡(luò)的自然進(jìn)化模型;同時(shí)在免疫網(wǎng)絡(luò)進(jìn)化過程中增加了抗體間的競(jìng)爭(zhēng)和協(xié)作操作,提高了網(wǎng)絡(luò)的動(dòng)態(tài)分析能力。后續(xù)實(shí)驗(yàn)中,分別采用常用的三組UCI數(shù)據(jù)和一幅紅樹林多光譜TM遙感圖像對(duì)算法加以驗(yàn)證,實(shí)驗(yàn)結(jié)果表明算法對(duì)遙感圖像有較高的分類效率,對(duì)UCI數(shù)據(jù)也有較好的分類效果,表明該算法是一種有效的數(shù)據(jù)分類方法。
[Abstract]:Based on the traditional artificial immune network, the typical strategy of multi-agent technology is integrated into the evolution of immune network.The algorithm introduces neighborhood clone selection, the operation process is from local to whole, it can simulate the natural evolution model of immune network more comprehensively, at the same time, the competition and cooperative operation among antibodies are increased in the evolution process of immune network.The dynamic analysis ability of the network is improved.In subsequent experiments, three groups of UCI data and a mangrove multispectral TM remote sensing image are used to verify the algorithm. The experimental results show that the algorithm has higher classification efficiency for remote sensing images and better classification effect for UCI data.It is shown that this algorithm is an effective method for data classification.
【作者單位】： 華僑大學(xué)工學(xué)院;華僑大學(xué)計(jì)算機(jī)學(xué)院;
【基金】：華僑大學(xué)研究生科研創(chuàng)新能力培育計(jì)劃資助項(xiàng)目
【分類號(hào)】：TP18;TP751

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本文編號(hào)：1721624