In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a so...In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a solution of the problem based upon the simulated annealing algorithm. This simulated annealing algorithm is indispensable for developing and testing highly refined empirical potential functions.展开更多
针对窃电问题严重阻碍建立公平、合理的用户秩序的问题,基于云计算的智能电网大数据处理平台SP-PPP(smart power system big data processing platform in cloud environment,SP-DPP),提出了融合自适应加权融合算法和深度置信网络DBN(De...针对窃电问题严重阻碍建立公平、合理的用户秩序的问题,基于云计算的智能电网大数据处理平台SP-PPP(smart power system big data processing platform in cloud environment,SP-DPP),提出了融合自适应加权融合算法和深度置信网络DBN(Deep Belief Networks,DBN)学习算法的反窃电系统,采用DBN逐层贪婪训练算法对大数据进行处理,并利用双层RBM结构,构建出DBN深度学习算法,对获取的电能计量窃电信息进行归一化处理,将获取的宏观高纬度数据信息转换为容易识别和计算的低纬度数据。实验表明,本研究的算法识别率高,稳定性能好。展开更多
基金Supported by the National Nataral Science Foundation of China(No.39980 0 0 5 )
文摘In this paper are reported the local minimum problem by means of current greedy algorithm for training the empirical potential function of protein folding on 8623 non-native structures of 31 globular proteins and a solution of the problem based upon the simulated annealing algorithm. This simulated annealing algorithm is indispensable for developing and testing highly refined empirical potential functions.
文摘针对窃电问题严重阻碍建立公平、合理的用户秩序的问题,基于云计算的智能电网大数据处理平台SP-PPP(smart power system big data processing platform in cloud environment,SP-DPP),提出了融合自适应加权融合算法和深度置信网络DBN(Deep Belief Networks,DBN)学习算法的反窃电系统,采用DBN逐层贪婪训练算法对大数据进行处理,并利用双层RBM结构,构建出DBN深度学习算法,对获取的电能计量窃电信息进行归一化处理,将获取的宏观高纬度数据信息转换为容易识别和计算的低纬度数据。实验表明,本研究的算法识别率高,稳定性能好。
