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
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
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
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。
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.
