针对原子优化算法寻优精度弱且易陷入局部极值的问题,本文从种群多样性、参数适应性和位置动态性角度提出一种融合混沌优化、振幅随机补偿和步长演变机制改进的原子搜索优化算法(improved atom search optimization,IASO),并将其成功应...针对原子优化算法寻优精度弱且易陷入局部极值的问题,本文从种群多样性、参数适应性和位置动态性角度提出一种融合混沌优化、振幅随机补偿和步长演变机制改进的原子搜索优化算法(improved atom search optimization,IASO),并将其成功应用于分类任务。首先,引入帐篷映射(Tent混沌)增强原子种群在搜索空间中的分布均匀性;其次,通过构建振幅函数对算法参数进行随机扰动并加入步长演变因子更新原子位置,以增强算法全局性和收敛性;最后,再将改进算法应用于误差反馈神经网络(BP神经网络)参数优化。通过与6种元启发式算法在20个基准测试函数下的数值实验对比表明:IASO不仅在求解多维基准函数上具有好的寻优性能,且在对BP神经网络参数进行优化时相较于2种对比算法具有更高的分类精度。展开更多
基波等效法是无线电能传输(wireless power transfer,WPT)技术的主要研究方法,该方法将整流性负载的基波阻抗等效为某一纯电阻,为系统的建模和分析提供基础。但该方法忽略整流性负载谐波阻抗的影响,使WPT系统的实际响应与理论分析结果...基波等效法是无线电能传输(wireless power transfer,WPT)技术的主要研究方法,该方法将整流性负载的基波阻抗等效为某一纯电阻,为系统的建模和分析提供基础。但该方法忽略整流性负载谐波阻抗的影响,使WPT系统的实际响应与理论分析结果存在较大的误差,从而影响系统的模型精度,限制WPT系统的进一步优化设计。该文以基于串/串并(series/series-parallel,S/SP)补偿网络的WPT系统为研究对象,分析利用基波等效法进行建模产生误差的原因,并提出一种基于迭代法的整流性负载基波以及各次谐波等效阻抗的精确计算方法。在此基础上,建立WPT系统的精确电路响应模型,所提模型可以有效表征发射线圈电流的畸变特性,并根据系统响应与补偿网络参数的关系获得系统逆变器开关损耗的优化设计方法。最后,搭建一台3kW的WPT系统样机,实验结果验证理论分析的正确性和可行性。展开更多
In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of ind...In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.展开更多
文摘针对原子优化算法寻优精度弱且易陷入局部极值的问题,本文从种群多样性、参数适应性和位置动态性角度提出一种融合混沌优化、振幅随机补偿和步长演变机制改进的原子搜索优化算法(improved atom search optimization,IASO),并将其成功应用于分类任务。首先,引入帐篷映射(Tent混沌)增强原子种群在搜索空间中的分布均匀性;其次,通过构建振幅函数对算法参数进行随机扰动并加入步长演变因子更新原子位置,以增强算法全局性和收敛性;最后,再将改进算法应用于误差反馈神经网络(BP神经网络)参数优化。通过与6种元启发式算法在20个基准测试函数下的数值实验对比表明:IASO不仅在求解多维基准函数上具有好的寻优性能,且在对BP神经网络参数进行优化时相较于2种对比算法具有更高的分类精度。
文摘基波等效法是无线电能传输(wireless power transfer,WPT)技术的主要研究方法,该方法将整流性负载的基波阻抗等效为某一纯电阻,为系统的建模和分析提供基础。但该方法忽略整流性负载谐波阻抗的影响,使WPT系统的实际响应与理论分析结果存在较大的误差,从而影响系统的模型精度,限制WPT系统的进一步优化设计。该文以基于串/串并(series/series-parallel,S/SP)补偿网络的WPT系统为研究对象,分析利用基波等效法进行建模产生误差的原因,并提出一种基于迭代法的整流性负载基波以及各次谐波等效阻抗的精确计算方法。在此基础上,建立WPT系统的精确电路响应模型,所提模型可以有效表征发射线圈电流的畸变特性,并根据系统响应与补偿网络参数的关系获得系统逆变器开关损耗的优化设计方法。最后,搭建一台3kW的WPT系统样机,实验结果验证理论分析的正确性和可行性。
基金Project(50734007) supported by the National Natural Science Foundation of China
文摘In the non-linear microwave drying process, the incremental improved back-propagation （BP） neural network and response surface methodology （RSM） were used to build a predictive model of the combined effects of independent variables （the microwave power, the acting time and the rotational frequency） for microwave drying of selenium-rich slag. The optimum operating conditions obtained from the quadratic form of the RSM are： the microwave power of 14.97 kW, the acting time of 89.58 min, the rotational frequency of 10.94 Hz, and the temperature of 136.407 ℃. The relative dehydration rate of 97.1895% is obtained. Under the optimum operating conditions, the incremental improved BP neural network prediction model can predict the drying process results and different effects on the results of the independent variables. The verification experiments demonstrate the prediction accuracy of the network, and the mean squared error is 0.16. The optimized results indicate that RSM can optimize the experimental conditions within much more broad range by considering the combination of factors and the neural network model can predict the results effectively and provide the theoretical guidance for the follow-up production process.
