A new optimization method is proposed to realize the synthesis of *** traditional optimization method takes all the variables of the duplexer into account,resulting in too many variables to be optimized when the order...
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A new optimization method is proposed to realize the synthesis of *** traditional optimization method takes all the variables of the duplexer into account,resulting in too many variables to be optimized when the order of the duplexer is too high,so it is not easy to fall into the local *** order to solve this problem,a new optimization strategy is proposed in this paper,that is,two-channel filters are optimized separately,which can reduce the number of optimization variables and greatly reduce the probability of results falling into local *** optimization method combines the self-adaptive differential evolution algorithm(SADE)with the Levenberg-Marquardt(lm)algorithm to get a global solution more easily and accelerate the optimization *** verify its practical value,we design a 5 G duplexer based on the proposed *** duplexer has a large external coupling,and how to achieve a feed structure with a large coupling bandwidth at the source is also *** experimental results show that the proposed optimization method can realize the synthesis of higher-order duplexers compared with the traditional methods.
The transmission dynamics of COVID-19 is investigated in this study. A SINDy-lm modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, ...
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The transmission dynamics of COVID-19 is investigated in this study. A SINDy-lm modeling method that can effectively balance model complexity and prediction accuracy is proposed based on data-driven technique. First, the Sparse Identification of Nonlinear Dynamical systems (SINDy) method is used to discover and describe the nonlinear functional relationship between the dynamic terms in the model in accordance with the observation data of the COVID-19 epidemic. Moreover, the Levenberg-Marquardt (lm) algorithm is utilized to optimize the obtained model for improving the accuracy of the SINDy algorithm. Second, the obtained model, which is consistent with the logistic model in mathematical form with small errors and high robustness, is leveraged to review the epidemic situation in China. Otherwise, the evolution of the epidemic in Australia and Egypt is predicted, which demonstrates that this method has universality for constructing the global COVID-19 model. The proposed model is also compared with the extreme learning machine (Elm), which shows that the prediction accuracy of the SINDy-lm method outperforms that of the Elm method and the generated model has higher sparsity.
High-voltage circuit breakers are the most important control and protection measures in power systems, and their reliable operation is critical to the safety and stability of power systems. However, the high-voltage c...
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ISBN:
(纸本)9789881563958
High-voltage circuit breakers are the most important control and protection measures in power systems, and their reliable operation is critical to the safety and stability of power systems. However, the high-voltage circuit breaker machinery often fails, the vibration signal of the high-voltage circuit breaker hides the rich fault information. The change of the vibration signal reflects the mechanical state of the circuit breaker, and the vibration vector feature vector extraction and classification is the most important problem in fault diagnosis. In this paper, a BP neural network and wavelet packet time - frequency entropy method based on lm optimization algorithm are proposed. The vibration signal of the circuit breaker is extracted and faulted. The vibration signal of the high voltage circuit breaker is decomposed by wavelet packet, and then the time-frequency entropy of the vibration signal is obtained as the eigenvector. The feature vector is input to the BP neural network optimized by lm to determine the working state and fault type of the circuit breaker. Experiments show that the BP neural network optimized by lmalgorithm and wavelet packet time-frequency entropy can be used to judge the mechanical failure of high voltage side circuit breaker more efficiently.
High-voltage circuit breakers are the most important control and protection measures in power systems, and their reliable operation is critical to the safety and stability of power systems. However, the high-voltage c...
详细信息
High-voltage circuit breakers are the most important control and protection measures in power systems, and their reliable operation is critical to the safety and stability of power systems. However, the high-voltage circuit breaker machinery often fails, the vibration signal of the high-voltage circuit breaker hides the rich fault information. The change of the vibration signal reflects the mechanical state of the circuit breaker, and the vibration vector feature vector extraction and classification is the most important problem in fault diagnosis. In this paper, a BP neural network and wavelet packet time-frequency entropy method based on lm optimization algorithm are proposed. The vibration signal of the circuit breaker is extracted and *** vibration signal of the high voltage circuit breaker is decomposed by wavelet packet, and then the time-frequency entropy of the vibration signal is obtained as the *** feature vector is input to the BP neural network optimized by lm to determine the working state and fault type of the circuit breaker. Experiments show that the BP neural network optimized by lmalgorithm and wavelet packet time-frequency entropy can be used to judge the mechanical failure of high voltage side circuit breaker more efficiently.
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