Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their businesses.For SMEs,owing to the inexistence...Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their businesses.For SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and costly.On the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of SMEs.Various data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier out-comes.With this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for SMEs.The proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data classification.The ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM method.The performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various metrics.The experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.展开更多
The optical storage microgrid system composed of power electronic converters is a small inertia system.Load switching and power supply intermittent will affect the stability of the direct current(DC)bus voltage.Aiming...The optical storage microgrid system composed of power electronic converters is a small inertia system.Load switching and power supply intermittent will affect the stability of the direct current(DC)bus voltage.Aiming at this problem,a virtual inertia optimal control strategy applied to optical storage microgrid is proposed.Firstly,a small signal model of the system is established to theoretically analyze the influence of virtual inertia and damping coefficient on DC bus voltage and to obtain the constraint range of virtual inertia and damping coefficient;Secondly,aiming at the defect that the Sailfish optimization algorithm is easy to premature maturity,a Sailfish optimization algorithm based on the leak-proof net and the cross-mutation propagation mechanism is proposed;Finally,the virtual inertia and damping coefficient of the system are optimized by the improved Sailfish algorithm to obtain the best control parameters.The simulation results in Matlab/Simulink show that the virtual inertia control optimized by the improved Sailfish algorithm improves the system inertia as well as the dynamic response and robustness of the DC bus voltage.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
针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)...针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)算法与扰动观察P&O(perturbation and observation)法混合控制的光伏系统最大功率跟踪策略。SFO算法同时使用旗鱼(捕食者)和沙丁鱼(猎物)2个种群,可保证粒子在全局空间探索。所提混合算法先利用SFO算法快速跟踪到最大功率点附近,再利用小步长P&O法对最大功率点进行精细搜索,最后利用分段步长的方法同时兼顾MPPT搜索速度和搜索精度的要求。仿真结果表明,所提混合控制策略有效提升了控制系统的响应速度及跟踪精度,提升了系统的稳定性。展开更多
为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolut...为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。展开更多
文摘Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their businesses.For SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and costly.On the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of SMEs.Various data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier out-comes.With this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for SMEs.The proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data classification.The ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM method.The performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various metrics.The experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.
基金the National Natural Science Foundation of China(52177184)。
文摘The optical storage microgrid system composed of power electronic converters is a small inertia system.Load switching and power supply intermittent will affect the stability of the direct current(DC)bus voltage.Aiming at this problem,a virtual inertia optimal control strategy applied to optical storage microgrid is proposed.Firstly,a small signal model of the system is established to theoretically analyze the influence of virtual inertia and damping coefficient on DC bus voltage and to obtain the constraint range of virtual inertia and damping coefficient;Secondly,aiming at the defect that the Sailfish optimization algorithm is easy to premature maturity,a Sailfish optimization algorithm based on the leak-proof net and the cross-mutation propagation mechanism is proposed;Finally,the virtual inertia and damping coefficient of the system are optimized by the improved Sailfish algorithm to obtain the best control parameters.The simulation results in Matlab/Simulink show that the virtual inertia control optimized by the improved Sailfish algorithm improves the system inertia as well as the dynamic response and robustness of the DC bus voltage.
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。
文摘针对传统的最大功率点跟踪MPPT(maximum power point tracking)方法在部分遮阴条件下陷入局部最优而失效,且常见的智能优化算法往往存在收敛精度差、收敛速度慢、系统稳定性不高等问题,提出1种基于旗鱼优化SFO(sailfish optimization)算法与扰动观察P&O(perturbation and observation)法混合控制的光伏系统最大功率跟踪策略。SFO算法同时使用旗鱼(捕食者)和沙丁鱼(猎物)2个种群,可保证粒子在全局空间探索。所提混合算法先利用SFO算法快速跟踪到最大功率点附近,再利用小步长P&O法对最大功率点进行精细搜索,最后利用分段步长的方法同时兼顾MPPT搜索速度和搜索精度的要求。仿真结果表明,所提混合控制策略有效提升了控制系统的响应速度及跟踪精度,提升了系统的稳定性。
文摘为了解决冲击噪声下长短时记忆(long short term memory,LSTM)神经网络调制信号识别方法抗冲击噪声能力弱和超参数难以确定的问题,本文提出了一种演化长短时记忆神经网络的调制识别方法。利用基于短时傅里叶变换的卷积神经网络(convolution neural network,CNN)去噪模型对数据集去噪;结合量子计算机制和旗鱼优化器(sailfish optimizer,SFO)设计了量子旗鱼算法(quantum sailfish algorithm,QSFA)去演化LSTM神经网络以获得最优的超参数;使用演化长短时记忆神经网络作为分类器进行自动调制信号识别。仿真结果表明,采用所设计的CNN去噪和演化长短时记忆神经网络模型,识别准确率有了大幅度的提高。量子旗鱼算法演化LSTM神经网络模型降低了传统LSTM神经网络容易陷于局部极小值或者过拟合的概率,当混合信噪比为0 dB,所提方法对11种调制信号的平均识别准确率达到90%以上。
