Retrieval of Refractivity Profile With Ground-Based Radio Occultation by Using an Improved Harmony Search Algorithm

Chiou, Mu-Min; Kiang, Jean‐Fu

doi:10.2528/pierm16052505

Cited by 3 publications

(2 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the displacement components obtained by EEMD decomposition, the highfrequency components are mainly caused by monitoring error noise, especially the IMF 1 component, so it is necessary to systematically denoise them [25]. e basic idea of the harmony search algorithm is a metaheuristic optimization algorithm that imitates the harmony tuning process played by a band.…”

Section: Harmony Search Algorithmmentioning

confidence: 99%

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Jiang

Xiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Ideological and political education is the most important way to cultivate students’ humanistic qualities, which can directly determine the development of other qualities. However, at present, the direction of ideological and political innovation in higher vocational colleges is vague. In response to this problem, this study proposes a model based on HS-EEMD-RNN. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the measured values, and then the recurrent neural network (RNN) is used to train each component and the remaining items. Finally, through the mapping relationship obtained by the model, the response prediction value of each component and the remaining items can be obtained. In the RNN training process, the harmony search (HS) algorithm is introduced to optimize it, and the noise is systematically denoised. Perturbation is used to obtain the optimal solution, thereby optimizing the weight and threshold of the RNN and improving the robustness of the model. The study found that, compared with EEMD-RNN, HS-EEMD-RNN has a better effect, because HS can effectively improve the training and fitting accuracy. The fitting accuracy of the HS-EEMD-RNN model after HS optimization is 0.9918. From this conclusion, the fitting accuracy of the HS-EEMD-RNN model is significantly higher than that of the EEMD-RNN model. In addition, four factors, career development, curriculum construction, community activities, and government support, have obvious influences on ideological and political classrooms in technical colleges. The use of recurrent neural networks in the research direction of deep and innovative research on the subject context of ideological and political classrooms can significantly improve the prediction accuracy of its development direction.

show abstract

Section: Harmony Search Algorithmmentioning

confidence: 99%

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Jiang

Xiang

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Simplified models based on a mass of historical detecting data have been studied extensively, for example, linear model, exponential model, double exponential model, Hopfield model, and subsection model [1][2][3]. A retrieval algorithm using ground-based global positioning system (GPS) is developed to establish the atmospheric refractivity profile model [4][5][6]. This method has high accuracy, but high searching volume and high time-consuming.…”

Section: Introductionmentioning

confidence: 99%

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

Ma¹,

Liu²,

Zhang³

2019

PIER M

View full text Add to dashboard Cite

A method based on the radial basis function neural network (RBFNN) is developed to fast establish the tropospheric refractivity profile model. Parameters of the RBFNN include SPREAD, and the number of training samples is optimized. The actual measured data of meteorological station at Qingdao city in China are used as test data to evaluate the performance of RBFNN. The simulation results show that the root mean squared error (RMSE) has a minimum of 0.81 when SPREAD is 8.1. The simulated values agree well with the test data which is observed by using the sounding balloon method. Finally, the tropospheric refractivity profile model of a selected area is established by using two different simulation methods. This paper attempts to propose a method to fast establish the tropospheric refractivity profile model which provides an available method to correct the atmospheric refraction error in radar applications.

show abstract

Estimating precipitable water vapour using the global positioning system and radio occultation over Ethiopian regions

Yehun

Gogie

Vermeer

et al. 2021

International Journal of Remote Sensing

View full text Add to dashboard Cite

Retrieval of Refractivity Profile With Ground-Based Radio Occultation by Using an Improved Harmony Search Algorithm

Cited by 3 publications

References 28 publications

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network

A Method for Fast Establishing Tropospheric Refractivity Profile Model Based on Radial Basis Function Neural Network

Estimating precipitable water vapour using the global positioning system and radio occultation over Ethiopian regions

Contact Info

Product

Resources

About