一種集成直覺(jué)模糊信息的激勵(lì)自適應(yīng)信任模型
doi: 10.11999/JEIT150750
基金項(xiàng)目:
國(guó)家自然科學(xué)基金(71361012, 61263018)，江西省教育廳科學(xué)技術(shù)研究項(xiàng)目(GJJ15267)，江西省高校人文社會(huì)科學(xué)研究項(xiàng)目(JC1338)，江西財(cái)經(jīng)大學(xué)青年基金
Incentive Adaptive Trust Model Based on Integrated Intuitionistic Fuzzy Information
Funds:
The National Natural Science Foundation of China (71361012, 61263018), The Science and Technology Project of Jiangxi province educational department of China (GJJ151601), The Colleges Humanities Social Science Research Project of Jiangxi Province (JC1338), The Young Foundation of Jiangxi University of Finance and Economics
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摘要: 針對(duì)現(xiàn)有信任機(jī)制不能很好表達(dá)信任的不確定性，且缺乏有效處理分布式網(wǎng)絡(luò)中存在的不誠(chéng)實(shí)推薦和策略性欺騙等問(wèn)題，該文提出一種集成直覺(jué)模糊信息的自適應(yīng)信任評(píng)估模型。為了激勵(lì)提供可信服務(wù)的實(shí)體，懲罰不良行為實(shí)體，該模型結(jié)合服務(wù)持續(xù)性強(qiáng)度和時(shí)間衰減因子，計(jì)算實(shí)體直接信任直覺(jué)模糊數(shù)。同時(shí)利用實(shí)體的推薦可信度和行為一致性過(guò)濾不誠(chéng)實(shí)推薦行為。除此之外，為了克服綜合信任聚合計(jì)算中權(quán)重確定較主觀的問(wèn)題，給出了一種新的自適應(yīng)權(quán)重分配方法。仿真實(shí)驗(yàn)表明，該模型在提高實(shí)體服務(wù)積極性和抑制惡意欺詐行為中，都有較好的適應(yīng)性和有效性。
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關(guān)鍵詞:
- 直覺(jué)模糊信息 /
- 信任模型 /
- 分布式網(wǎng)絡(luò) /
- 推薦可信度
Abstract: Existing trust models can not effectively express the uncertainty of trust relationship and deal with such issues as dishonest feedbacks and strategic frauds from malicious entities in the distributed network, an adaptive trust model based on aggregating Intuitionistic Fuzzy Information (IFI) is proposed. Firstly, in order to incentivize entities providing trustworthy service and punish entities taking along malicious behavior, an approach on aggregating IFI is constructed to compute the direct trust intuitionistic fuzzy numbers which contain the latest permanence factor and the time decay factor. Then, the recommendation credibility and uniformity are defined to detect dishonest recommendation. Subsequent, an adaptive weighted approach is developed to avoid distributing the weights of direct and indirect trust subjectively. The simulation experiments demonstrate that the proposed model not only is robust on malicious attacks, but also has better adaptability and effectiveness. -
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