条件估值法(ContingentValuation Method,CVM),通过询问人们对于环境质量改善的支付意愿(W illingness To Pay,WTP)或受到损害后的受偿意愿(W illingness To Accept,WTA)来评估环境物品或服务的价值。虽然对CVM的准确性存在争议,但这一...条件估值法(ContingentValuation Method,CVM),通过询问人们对于环境质量改善的支付意愿(W illingness To Pay,WTP)或受到损害后的受偿意愿(W illingness To Accept,WTA)来评估环境物品或服务的价值。虽然对CVM的准确性存在争议,但这一方法正被越来越广泛地应用,人们认识到这一方法能够解决许多无法解决的问题。空气污染一直是澳门的环境问题,空气污染造成的损害是多方面的。本研究分2次进行,2002年12月SARS(Severe Acute Respiratory Syndrome)爆发前,进行了WTP的调查,打电话调查样本1 600个,回收有效问卷720份;2004年3月SARS爆发后,又进行了WTA调查,打电话调查样本1 336个,回收有效问卷543份。本研究旨在采用CVM中之WTP和WTA方法,分析比较SARS爆发前后,居民对澳门空气污染损失的意愿价值的变化情况,探讨WTP和WTA两种研究方法的估值差异,为城市环境管理提供决策依据。本研究的特点:①对调查的误差进行了统计学分析;②CVM的调查采用支付意愿和受偿意愿相结合的对比研究。经济分析结果表明,2002年SARS爆发前,以WTP分析得出澳门空气污染的年经济损失保守估计值为3.77亿MOP(澳门元,1美元=8.033MOP)、占当年GDP的0.69%;2004年SARS爆发后,以WTA法分析得出澳门空气污染的年经济损失最高估计值为14.32亿MOP/年,占当年GDP的2.2%。研究表明,SARS爆发后,居民的环护意识有不同程度的提高。展开更多
Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness...Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.展开更多
本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析...本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析结果 .结果显示HS K WTA要比Winstrons更好 ,更容易硬件实现 ,更稳定 ,尤其所取的数集中的数较大时 .展开更多
基金supported by the Fundamental Research Funds for the Central Universities(NZ2013306)the Funding of Jiangsu Innovation Program for Graduate Education(CXLX11 0203)
文摘Cooperative jamming weapon-target assignment （CJWTA） problem is a key issue in electronic countermeasures （ECM）. Some symbols which relevant to the CJWTA are defined firstly. Then, a formulation of jamming fitness is presented. Final y, a model of the CJWTA problem is constructed. In order to solve the CJWTA problem efficiently, a self-adaptive learning based discrete differential evolution （SLDDE） algorithm is proposed by introduc-ing a self-adaptive learning mechanism into the traditional discrete differential evolution algorithm. The SLDDE algorithm steers four candidate solution generation strategies simultaneously in the framework of the self-adaptive learning mechanism. Computa-tional simulations are conducted on ten test instances of CJWTA problem. The experimental results demonstrate that the proposed SLDDE algorithm not only can generate better results than only one strategy based discrete differential algorithms, but also outper-forms two algorithms which are proposed recently for the weapon-target assignment problems.
文摘本文提出一种新的K Winners Take All神经网络 :High Speed K Winners Take All(HS K WTA) .HS K WTA是以竞争学习算法为基础 .HS K WTA能够从任何一个数集中 ,识别出K个较大的数 ,或较小的数 .本文给出HS K WTA算法及算法复杂性的分析结果 .结果显示HS K WTA要比Winstrons更好 ,更容易硬件实现 ,更稳定 ,尤其所取的数集中的数较大时 .
