Generalized Regression Neural Network Optimized by Genetic Algorithm for Solving Out-of-Sample Extension Problem in Supervised Manifold Learning

2019 ◽  
Vol 50 (3) ◽  
pp. 2567-2593
Author(s):  
Hong-Bing Huang ◽  
Zhi-Hong Xie
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Manifold Learning ◽  
Extension Problem ◽  
Generalized Regression Neural Network ◽  
Out Of Sample ◽  
Generalized Regression

The Distribution of Teaching Staff in Colleges and Universities based on Neuropsychology

2021 ◽  
Vol 7 (5) ◽  
pp. 4682-4692
Author(s):  
Ruolin Yang ◽  
Dan Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Colleges And Universities ◽  
Quality Education ◽  
Generalized Regression Neural Network ◽  
Teaching Staff ◽  
Actual Situation ◽  
Teacher Resources ◽  
Calculation Process ◽  
Generalized Regression

Objectives: At present, quality education has gradually been recognized by the whole society, and a consensus has been reached on its importance, which has put forward stricter requirements for the distribution of faculty in universities. Methods: In this paper, based on neuropsychology, the distribution of teaching staff in colleges and universities was studied, and the model of talent evaluation and distribution was constructed. Results: Firstly, the generalized regression neural network was optimized by genetic algorithm. Then, the genetic algorithm’s generalized regression neural network calculation process was designed. Conclusion: Finally, with the example of teacher resources in a university, the algorithm in this paper was tested. The results show that the results of the generalized regression neural network optimized by genetic algorithm can match the actual situation very well, and the method is feasible with certain advantages.

Supervised Imagery Classification Based on Hierarchical Macro Manifold

2014 ◽  
Vol 556-562 ◽  
pp. 4843-4846
Author(s):  
Hong Bing Huang
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Dimensionality Reduction ◽  
Data Visualization ◽  
Manifold Learning ◽  
Supervised Classification ◽  
Generalized Regression Neural Network ◽  
Sample Problem ◽  
Out Of Sample ◽  
The Given

Manifold learning has made many successful applications in the fields of dimensionality reduction, pattern recognition, and data visualization. In this paper we proposed hierarchical macro manifold (HMM) for the purpose of supervised classification. We construct hierarchical macro manifold based on the given training sets. The generalized regression neural network is employed to solve the out-of-sample problem. Experimental results demonstrate the feasibility and effectiveness of our proposed approach.

An approach to carbon emissions prediction using generalized regression neural network improved by genetic algorithm

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhida Guo ◽  
Jingyuan Fu
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Total Carbon ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Target Values ◽  
Generalized Regression

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era, and actively responding to climate change policy. Through the analysis of the application of the generalized regression neural network (GRNN) in prediction, this paper improved the prediction method of GRNN. Genetic algorithm (GA) was adopted to search the optimal smooth factor as the only factor of GRNN, which was then used for prediction in GRNN. During the prediction of carbon dioxide emissions using the improved method, the increments of data were taken into account. The target values were obtained after the calculation of the predicted results. Finally, compared with the results of GRNN, the improved method realized higher prediction accuracy. It thus offers a new way of predicting total carbon dioxide emissions, and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

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