Research of GPS Elevation Conversion Based on Least Square Support Vector Machine and BP Neural Network

Liu, Li Long; Zhang, Teng Xu; Zhou, Miao; Wang, Wei; Huang, Liang Ke

doi:10.4028/www.scientific.net/amm.501-504.2166

Cited by 5 publications

(4 citation statements)

References 1 publication

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“…The SVM regression performance is primarily affected by the type of kernel function and model parameters 39 . The kernel function converts nonlinear inseparable samples into linear separable samples in the feature space.…”

Section: Methodsmentioning

confidence: 99%

Optimization and control of the light environment for greenhouse crop production

Xin

Zhang

et al. 2019

Sci Rep

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Optimization and control of the greenhouse light environment is key to increasing crop yield and quality. However, the light saturation point impacts the efficient use of light. Therefore, the dynamic acquisition of the light saturation point that is influenced by changes in temperature and CO 2 concentration is an important challenge for the development of greenhouse light environment control system. In view of this challenge, this paper describes a light environment optimization and control model based on a crop growth model for predicting cucumber photosynthesis. The photosynthetic rate values for different photosynthetic photon flux densities (PPFD), CO 2 concentration, and temperature conditions provided to cucumber seedlings were obtained by using an LI-6400XT portable photosynthesis system during multi-factorial experiments. Based on the measured data, photosynthetic rate predictions were determined. Next, a support vector machine(SVM) photosynthetic rate prediction model was used to obtain the light response curve under other temperatures and CO 2 conditions. The light saturation point was used to establish the light environment optimization and control model and to perform model validation. The slope of the fitting straight line comparing the measured and predicted light saturation point was 0.99, the intercept was 23.46 and the coefficient of determination was 0.98. The light control model was able to perform dynamic acquisition of the light saturation point and provide a theoretical basis for the efficient and accurate control of the greenhouse light environment.

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Section: Methodsmentioning

confidence: 99%

Optimization and control of the light environment for greenhouse crop production

Xin

Zhang

et al. 2019

Sci Rep

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“…Several artificial intelligence algorithm models are widely used, including the BP Neural Network model, SVM model, and Linear Regression model. In recent years, research has been focused on SVM, which is an excellent classifier with strong generalization ability and relatively high accuracy ( Liu et al, 2014 ). Zhang et al used a comprehensive GEO database and SVM to detect breast cancer biomarkers to distinguish between normal samples and cancer samples; the discriminant model using cross validation to the accuracy, at the same time using cross-validation score as evaluation standard, and the SVM algorithm and other algorithms are compared.…”

Section: Discussionmentioning

confidence: 99%

Establishment of a Lung Cancer Discriminative Model Based on an Optimized Support Vector Machine Algorithm and Study of Key Targets of Wogonin in Lung Cancer

et al. 2021

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An optimized support vector machine model was used to construct a lung cancer diagnosis model based on serological indicators, and a molecular regulation model of Wogonin, a component of Scutellaria baicalensis, was established. Serological indexes of patients were collected, the grid search method was used to identify the optimal penalty coefficient C and parameter g of the support vector machine model, and the benign and malignant auxiliary diagnosis model of isolated pulmonary nodules based on serological indicators was established. The regulatory network and key targets of Wogonin in lung cancer were analyzed by network pharmacology, and key targets were detected by western blot. The relationship between serological susceptibility genes and key targets of Wogonin was established, and the signaling pathway of Wogonin regulating lung cancer was constructed. After support vector machine parameter optimization (C = 90.597, g = 32), the accuracy of the model was 90.8333%, with nine false positives and two false negative cases. Ontology functional analysis of 67 common genes between Wogonin targets and lung cancer–related genes showed that the targets were associated with biological processes involved in peptidye-serine modification and regulation of protein kinase B signaling; cell components in the membrane raft and chromosomal region; and molecular function in protein serine/threonine kinase activity and heme binding. Kyoto Encyclopedia of Genes and Genomes analysis showed that the regulation pathways involved the PI3K-Akt signaling pathway, ERBB signaling pathway, and EGFR tyrosine kinase inhibitor resistance. In vitro analyses using lung cancer cells showed that Wogonin led to significantly increased levels of cleaved caspase-3 and Bad and significantly decreased Bcl-2 expression in a concentration-dependent manner. ErbB4 expression also significantly decreased in lung cancer cells after treatment with Wogonin. A regulatory network of Wogonin regulating lung cancer cell apoptosis was constructed, including the participation of serological susceptibility genes. There is a certain regulatory effect between the serological indexes that can be used in the diagnosis of lung cancer and the key targets of Chinese herbal medicine treatment of lung cancer, which provides a new idea for the diagnosis, treatment and prognosis of clinical lung cancer.

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“…Because of the influence of economic society and history, the taxation system in our country is a complex system influenced by many factors, with all influencing factors interacting and influencing with each other, and many influencing factors have a large number of factors of uncertainty, which lead to a greatly increased complexity of tax revenue forecasting in our country [6]. Tax revenue prediction is the basis for the scientific formulation of tax policies, it's also an essential condition for formulating on fiscal budgets and carrying out macro regulation in the country, and it will contributes to the healthy development of the economy and society [11]. Therefore, scholars have done a lot of research on tax forecasting.…”

Section: Shaoxin Zheng Wanchun Fumentioning

confidence: 99%

“…In this formula, y presents the input variable; w presents the we represents the bias tensor. In order to reduce the structural risk, LSSVM adopts the optima (11) 8 Finally, an exponential transformation and a geometric average generating transformation are performed to restore the predicted value.…”

Section: Lssvm Algorithmmentioning

confidence: 99%

Research on the Prediction Model of Chinese Tax Revenue Based on GM(1,1) and LSSVM

Zhang,

Zheng,

2023

ITC

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In view of the complex influencing factors of tax revenue, the highly non-linear relationship among the influencing factors and the difficulty in predicting tax revenue, this paper proposes to use GM (1, 1) Combined with LSSVM, and it calculates the tax forecasting of China. This paper selects the proportion of the first industry, the ratio of import and export trade to GDP, GDP, the number of urban employment population, the proportion of residents' disposable income and tax revenue in fiscal revenue as the influencing factors, and uses GM (1, 1) and LSSVM respectively to predict the tax revenue of our country, establishes the quadratic programming model to determine the optimal combination weight for the formation of the combination predicting model of tax revenue in our country, make an empirical analysis with the tax revenue of our country from 2000 to 2018 as the research object, and compare the prediction results with LSSVM model, GM (1,1) model and improved GM (1,1) model. The results show that the prediction model of China's tax revenue based on GM (1,1) and LSSVM has a high fitting accuracy with the test set, which can reflect the complex non-linear relationship between various factors. It is of great significance for the development of prediction on Chinese tax revenue and the formulation of a scientific and effective national financial budget.

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Research of GPS Elevation Conversion Based on Least Square Support Vector Machine and BP Neural Network

Cited by 5 publications

References 1 publication

Optimization and control of the light environment for greenhouse crop production

Optimization and control of the light environment for greenhouse crop production

Establishment of a Lung Cancer Discriminative Model Based on an Optimized Support Vector Machine Algorithm and Study of Key Targets of Wogonin in Lung Cancer

Research on the Prediction Model of Chinese Tax Revenue Based on GM(1,1) and LSSVM

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