Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,...Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.展开更多
This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time ...This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.展开更多
The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and co...The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.展开更多
基金Supported by the Anhui Province Sports Health Information Monitoring Technology Engineering Research Center Open Project (KF2023012)。
文摘Deep learning algorithm is an effective data mining method and has been used in many fields to solve practical problems.However,the deep learning algorithms often contain some hyper-parameters which may be continuous,integer,or mixed,and are often given based on experience but largely affect the effectiveness of activity recognition.In order to adapt to different hyper-parameter optimization problems,our improved Cuckoo Search(CS)algorithm is proposed to optimize the mixed hyper-parameters in deep learning algorithm.The algorithm optimizes the hyper-parameters in the deep learning model robustly,and intelligently selects the combination of integer type and continuous hyper-parameters that make the model optimal.Then,the mixed hyper-parameter in Convolutional Neural Network(CNN),Long-Short-Term Memory(LSTM)and CNN-LSTM are optimized based on the methodology on the smart home activity recognition datasets.Results show that the methodology can improve the performance of the deep learning model and whether we are experienced or not,we can get a better deep learning model using our method.
基金the National Natural Science Foundation of China(No.61571146)the Fundamental Research Funds for the Central Universities of China(No.HEUCFP201769)
文摘This paper formulates a new framework to estimate the target position by adopting cuckoo search(CS)positioning algorithm. Addressing the nonlinear optimization problem is a crucial spot in the location system of time difference of arrival(TDOA). With the application of the Levy flight mechanism, the preferential selection mechanism and the elimination mechanism, the proposed approach prevents positioning results from falling into local optimum. These intelligent mechanisms are useful to ensure the population diversity and improve the convergence speed. Simulation results demonstrate that the cuckoo localization algorithm has higher locating precision and better performance than the conventional methods. Compared with particle swarm optimization(PSO) algorithm and Newton iteration algorithm, the proposed method can obtain the Cram′er-Rao lower bound(CRLB) and quickly achieve the global optimal solutions.
基金supported by the National Natural Science Foundation of China(51875465)
文摘The present study proposed an enhanced cuckoo search(ECS) algorithm combined with artificial neural network(ANN) as the surrogate model to solve structural reliability problems. In order to enhance the accuracy and convergence rate of the original cuckoo search(CS) algorithm, the main parameters namely, abandon probability of worst nests paand search step sizeα0 are dynamically adjusted via nonlinear control equations. In addition, a global-best guided equation incorporating the information of global best nest is introduced to the ECS to enhance its exploitation. Then, the proposed ECS is linked to the well-trained ANN model for structural reliability analysis. The computational capability of the proposed algorithm is validated using five typical structural reliability problems and an engineering application. The comparison results show the efficiency and accuracy of the proposed algorithm.
