Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel...Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.展开更多
SF_(6)电气设备内部的分解组分可以通过可调谐吸收光谱技术进行检测,其中CO_(2)浓度反映了设备内部的绝缘缺陷情况。因此,通过准确测量CO_(2)浓度可以及时发现设备潜在的绝缘故障。为克服传统最小二乘法浓度反演模型稳定性较差的问题,...SF_(6)电气设备内部的分解组分可以通过可调谐吸收光谱技术进行检测,其中CO_(2)浓度反映了设备内部的绝缘缺陷情况。因此,通过准确测量CO_(2)浓度可以及时发现设备潜在的绝缘故障。为克服传统最小二乘法浓度反演模型稳定性较差的问题,文中基于改进的旗鱼优化算法(Improved Sailed Fish Optimizer,ISFO)与核极限学习机(Kernel Based Extreme Learning Machine,KELM)建立了ISFO-KELM气体浓度反演模型。利用多策略初始化方法、Levy随机步长、柯西变异和自适应t分布变异等技术提升了旗鱼优化算法寻优能力和跳出局部最优解能力。实验结果表明,该模型具有高精度和鲁棒性,并且在稳定性和泛化能力方面优于最小二乘法、极限学习机、反向传播(Back Propagation,BP)神经网络等传统方法,对评估SF_(6)电气设备运行状态具有重要意义。展开更多
基金Sponsored by the National Natural Science Foundation of China(51006052)
文摘Extreme learning machine(ELM) has attracted much attention in recent years due to its fast convergence and good performance.Merging both ELM and support vector machine is an important trend,thus yielding an ELM kernel.ELM kernel based methods are able to solve the nonlinear problems by inducing an explicit mapping compared with the commonly-used kernels such as Gaussian kernel.In this paper,the ELM kernel is extended to the least squares support vector regression(LSSVR),so ELM-LSSVR was proposed.ELM-LSSVR can be used to reduce the training and test time simultaneously without extra techniques such as sequential minimal optimization and pruning mechanism.Moreover,the memory space for the training and test was relieved.To confirm the efficacy and feasibility of the proposed ELM-LSSVR,the experiments are reported to demonstrate that ELM-LSSVR takes the advantage of training and test time with comparable accuracy to other algorithms.
文摘SF_(6)电气设备内部的分解组分可以通过可调谐吸收光谱技术进行检测,其中CO_(2)浓度反映了设备内部的绝缘缺陷情况。因此,通过准确测量CO_(2)浓度可以及时发现设备潜在的绝缘故障。为克服传统最小二乘法浓度反演模型稳定性较差的问题,文中基于改进的旗鱼优化算法(Improved Sailed Fish Optimizer,ISFO)与核极限学习机(Kernel Based Extreme Learning Machine,KELM)建立了ISFO-KELM气体浓度反演模型。利用多策略初始化方法、Levy随机步长、柯西变异和自适应t分布变异等技术提升了旗鱼优化算法寻优能力和跳出局部最优解能力。实验结果表明,该模型具有高精度和鲁棒性,并且在稳定性和泛化能力方面优于最小二乘法、极限学习机、反向传播(Back Propagation,BP)神经网络等传统方法,对评估SF_(6)电气设备运行状态具有重要意义。
