机译:环境数据驱动模型的输入变量选择算法的评估框架
Pillar of Engineering Systems and Design, Singapore University of Technology and Design, 20 Dover Drive, 138682, Singapore;
School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australia;
School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australia;
Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza L. da Vinci, 32, 20133, Milan, Italy;
School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australia;
School of Civil, Environmental, and Mining Engineering, University of Adelaide, SA, 5005, Australia,Department of Environment, Water and Natural Resources, GPO Box 2384, Adelaide, SA, 5001, Australia;
Input variable selection; Data-driven modelling; Evaluation framework; Large environmental datasets; Artificial neural networks;
机译:基于二进制编码粒子群优化和极限学习机的降雨径流建模数据驱动输入变量选择
机译:数据驱动的空气冷凝器性能评估:模型和输入变量选择比较
机译:改进的基于PMI的人工神经网络和其他数据驱动的环境和水资源模型的输入变量选择方法
机译:数据驱动的软传感器设计的输入变量选择标准
机译:使用数据驱动的能源和乘员对室内环境质量的满意度预测模型(IEQ),评估获得能源和环境设计(LEED)认证的设施的领导绩效。
机译:在建模问题中将输入选择算法扩展到低质量数据的过程及其在上载作业的自动评分中的应用
机译:环境数据驱动模型的输入变量选择算法的评估框架