In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data ...In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.展开更多
针对复杂装备体系(Complex Equipment System-of-systems,CES)优化设计中指标变量多、仿真依赖性强、易陷入局部最优的问题,提出一种基于正向解析式和多目标博弈理论(Multi-Objective Game Theory,MOGT)优化算法的CES优化设计方法。为提...针对复杂装备体系(Complex Equipment System-of-systems,CES)优化设计中指标变量多、仿真依赖性强、易陷入局部最优的问题,提出一种基于正向解析式和多目标博弈理论(Multi-Objective Game Theory,MOGT)优化算法的CES优化设计方法。为提升CES优化设计的可解释性,构建任务级—能力级—装备级的评估指标体系;在此基础上,基于装备机理和效用函数表征装备评估指标与作战能力之间的正向映射关系,并利用相邻优属度熵权法计算各指标权重;通过正向解析式与约束条件建立多目标优化模型,并采用MOGT优化算法获得最佳优化结果。以某作战推演平台中防空攻防想定为例,开展算例评估与验证分析。研究结果表明,该方法能够实现CES中最优设计方案的求解,可显著提高设计效率和降低设计成本,为下一代装备发展论证、设计评估和作战试验提供了基础性工作。展开更多
基金Heilongjiang Natural Science Foundation (F0318).
文摘In the paper, a method of building mathematic model employing genetic multilayer feed forward neural network is presented, and the quantitative relationship of chemical measured values and near-infrared spectral data is established. In the paper, quantitative mathematic model related chemical assayed values and near-infrared spectral data is established by means of genetic multilayer feed forward neural network, acquired near-infrared spectral data are taken as input of network with the content of five kinds of fat acids tested from chemical method as output, weight values of multilayer feed forward neural network are trained by genetic algorithms and detection model of neural network of soybean is built. A kind of multilayer feed forward neural network trained by genetic algorithms is designed in the paper. Through experiments, all the related coefficients of five fat acids can approach 0.9 which satisfies the preliminary test of soybean breeding.
文摘针对复杂装备体系(Complex Equipment System-of-systems,CES)优化设计中指标变量多、仿真依赖性强、易陷入局部最优的问题,提出一种基于正向解析式和多目标博弈理论(Multi-Objective Game Theory,MOGT)优化算法的CES优化设计方法。为提升CES优化设计的可解释性,构建任务级—能力级—装备级的评估指标体系;在此基础上,基于装备机理和效用函数表征装备评估指标与作战能力之间的正向映射关系,并利用相邻优属度熵权法计算各指标权重;通过正向解析式与约束条件建立多目标优化模型,并采用MOGT优化算法获得最佳优化结果。以某作战推演平台中防空攻防想定为例,开展算例评估与验证分析。研究结果表明,该方法能够实现CES中最优设计方案的求解,可显著提高设计效率和降低设计成本,为下一代装备发展论证、设计评估和作战试验提供了基础性工作。
