When the Grover’s algorithm is applied to search an unordered database, the successful probability usually decreases with the increase of marked items. In order to solve this problem, an adaptive phase matching is pr...When the Grover’s algorithm is applied to search an unordered database, the successful probability usually decreases with the increase of marked items. In order to solve this problem, an adaptive phase matching is proposed. With application of the new phase matching, when the fraction of marked items is greater , the successful probability is equal to 1 with at most two Grover iterations. The validity of the new phase matching is verified by a search example.展开更多
Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convol...Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.展开更多
针对目前对Gram-Schmidt(G-S)自适应零点控制算法的研究大多只停留在理论研究阶段,还没有将其应用于低截获概率(LPI,Low Probability of Intercept)雷达设计,提出一种改进的G-S自适应零点控制算法应用于连续波(CW,Continuous Wave)体制...针对目前对Gram-Schmidt(G-S)自适应零点控制算法的研究大多只停留在理论研究阶段,还没有将其应用于低截获概率(LPI,Low Probability of Intercept)雷达设计,提出一种改进的G-S自适应零点控制算法应用于连续波(CW,Continuous Wave)体制LPI雷达。该算法可以把接收信号中的目标信息去除,只留下干扰信号,进而进行正交化处理,从而防止目标信息被当作干扰抑制掉。阵列方向图、零陷深度和误差分析等仿真结果表明:该算法在保证较快的收敛速度和较好的稳定性的基础上,相对于传统的G-S正交法有10 dB以上的零陷加深,从而验证了该算法能有效提升雷达的LPI性能。展开更多
为了解决无线传感器网络Qo S(Quality of Service,Qo S)路由在寻找最优路径时要满足时延、抖动、能量等多个约束条件的问题,提出一种新的自适应蚁群优化算法,该算法有两方面的自适应策略。将信息素挥发因子ρ设置为动态自适应,在自适应...为了解决无线传感器网络Qo S(Quality of Service,Qo S)路由在寻找最优路径时要满足时延、抖动、能量等多个约束条件的问题,提出一种新的自适应蚁群优化算法,该算法有两方面的自适应策略。将信息素挥发因子ρ设置为动态自适应,在自适应因子μ作用下动态变化,增强算法的寻优能力,避免算法陷入局部最优;以多约束为条件建立加权的适应度函数,通过适应度函数值与自适应因子μ共同影响路径上的信息素更新,增强算法的收敛速度。通过仿真实验表明,该算法在满足多约束条件方面具有良好的效果。展开更多
文摘When the Grover’s algorithm is applied to search an unordered database, the successful probability usually decreases with the increase of marked items. In order to solve this problem, an adaptive phase matching is proposed. With application of the new phase matching, when the fraction of marked items is greater , the successful probability is equal to 1 with at most two Grover iterations. The validity of the new phase matching is verified by a search example.
基金Natural Science Foundation of Shandong Province,China(Grant No.ZR202111230202).
文摘Hyperspectral image classification stands as a pivotal task within the field of remote sensing,yet achieving highprecision classification remains a significant challenge.In response to this challenge,a Spectral Convolutional Neural Network model based on Adaptive Fick’s Law Algorithm(AFLA-SCNN)is proposed.The Adaptive Fick’s Law Algorithm(AFLA)constitutes a novel metaheuristic algorithm introduced herein,encompassing three new strategies:Adaptive weight factor,Gaussian mutation,and probability update policy.With adaptive weight factor,the algorithmcan adjust theweights according to the change in the number of iterations to improve the performance of the algorithm.Gaussianmutation helps the algorithm avoid falling into local optimal solutions and improves the searchability of the algorithm.The probability update strategy helps to improve the exploitability and adaptability of the algorithm.Within the AFLA-SCNN model,AFLA is employed to optimize two hyperparameters in the SCNN model,namely,“numEpochs”and“miniBatchSize”,to attain their optimal values.AFLA’s performance is initially validated across 28 functions in 10D,30D,and 50D for CEC2013 and 29 functions in 10D,30D,and 50D for CEC2017.Experimental results indicate AFLA’s marked performance superiority over nine other prominent optimization algorithms.Subsequently,the AFLA-SCNN model was compared with the Spectral Convolutional Neural Network model based on Fick’s Law Algorithm(FLA-SCNN),Spectral Convolutional Neural Network model based on Harris Hawks Optimization(HHO-SCNN),Spectral Convolutional Neural Network model based onDifferential Evolution(DE-SCNN),SpectralConvolutionalNeuralNetwork(SCNN)model,and SupportVector Machines(SVM)model using the Indian Pines dataset and PaviaUniversity dataset.The experimental results show that the AFLA-SCNN model outperforms other models in terms of Accuracy,Precision,Recall,and F1-score on Indian Pines and Pavia University.Among them,the Accuracy of the AFLA-SCNN model on Indian Pines reached 99.875%,and the Accuracy on PaviaUniversity reached 98.022%.In conclusion,our proposed AFLA-SCNN model is deemed to significantly enhance the precision of hyperspectral image classification.
文摘针对目前对Gram-Schmidt(G-S)自适应零点控制算法的研究大多只停留在理论研究阶段,还没有将其应用于低截获概率(LPI,Low Probability of Intercept)雷达设计,提出一种改进的G-S自适应零点控制算法应用于连续波(CW,Continuous Wave)体制LPI雷达。该算法可以把接收信号中的目标信息去除,只留下干扰信号,进而进行正交化处理,从而防止目标信息被当作干扰抑制掉。阵列方向图、零陷深度和误差分析等仿真结果表明:该算法在保证较快的收敛速度和较好的稳定性的基础上,相对于传统的G-S正交法有10 dB以上的零陷加深,从而验证了该算法能有效提升雷达的LPI性能。
文摘为了解决无线传感器网络Qo S(Quality of Service,Qo S)路由在寻找最优路径时要满足时延、抖动、能量等多个约束条件的问题,提出一种新的自适应蚁群优化算法,该算法有两方面的自适应策略。将信息素挥发因子ρ设置为动态自适应,在自适应因子μ作用下动态变化,增强算法的寻优能力,避免算法陷入局部最优;以多约束为条件建立加权的适应度函数,通过适应度函数值与自适应因子μ共同影响路径上的信息素更新,增强算法的收敛速度。通过仿真实验表明,该算法在满足多约束条件方面具有良好的效果。
