scholarly journals Prediction Algorithm of Collaborative Innovation Capability of High-End Equipment Manufacturing Enterprises Based on Random Forest

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Zhenhong Xiao ◽  
Jianbang Shi ◽  
Rui Tan ◽  
Junyi Shen
Keyword(s):  
Random Forest ◽  
Asset Management ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Empirical Evaluation ◽  
Listed Companies ◽  
Supervised Machine Learning ◽  
Prediction Algorithm ◽  
Manufacturing Enterprises ◽  
Equipment Manufacturing

This paper studies the competitiveness of listed companies in high-end equipment manufacturing industry by using random forest. Random forest is a supervised machine learning algorithm that is actually based on the regression and classification. It takes some important decisions that are always based upon the set of samples. It counts majority for the classification purposes while it takes an average for the regression. For empirical analysis, 88 listed companies are selected. It is found that there are great differences in comprehensive competitiveness among industries. Enterprise scale accounts for a high proportion in the comprehensive competitiveness, and its score often affects the comprehensive strength; and the gap between companies in the same industry is also obvious. The empirical evaluation results of this paper provide three enlightenments for enterprises to improve their comprehensive competitiveness, such as seizing the strategic opportunity to expand the market, expand the scale of enterprises, improve asset management, and narrow the industry gap.

Grey relation analysis of 4S service pilot in high-end equipment manufacturing industry

2021 ◽  
pp. 1-13
Author(s):  
Chunyue Xiao ◽  
Jian Sun
Keyword(s):  
Manufacturing Industry ◽  
Relevant Literature ◽  
Competitive Advantages ◽  
Product Model ◽  
Model Framework ◽  
Manufacturing Enterprises ◽  
Customer Interaction ◽  
Cooperative Production ◽  
Equipment Manufacturing ◽  
Equipment Manufacturing Industry

Servitization has a significant impact on the upgrading and reform of the equipment manufacturing industry. From the perspective of application of high-end servitization theory in business practice of equipment manufacturing industry, based on the review of relevant literature, this paper analyzes the concept of integration delay strategy mechanism of cooperative production between enterprises and customers, and thus constructs the theoretical model framework of 4S service pilot high-end equipment manufacturing product-customer interaction experience. With the Liaoning equipment manufacturing industry as a case for quantitative analysis, the feasibility of using delay strategy in the 4S service pilot program is demonstrated, and the three-stage development plan of Liaoning 4S service pilot is outlined. The results show that: At present, in the trend of servitization of China’s equipment manufacturing enterprises, 4S service pilot high-end manufacturing product model enables equipment manufacturing enterprises to delay production time and produce according to customer orders, improve service efficiency and optimize resource allocation, and help enterprises to obtain new exclusive competitive advantages.

scholarly journals Deducing of Optimal Machine Learning Algorithms for Heterogeneity

2021 ◽  
Author(s):  
Omar Alfarisi ◽  
Zeyar Aung ◽  
Mohamed Sassi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Synthetic Data ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneity, we identified Random Forest, among others, to be the best algorithm.

Research on financing efficiency and influencing factors of equipment manufacturing industry——Regression model based on SFA panel data

2021 ◽  
pp. 1-10
Author(s):  
Qian Wang ◽  
Jianxu Wang ◽  
Hua Li ◽  
Xiang Li
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Influencing Factors ◽  
Manufacturing Industry ◽  
Turnover Rate ◽  
Negative Impact ◽  
Frontier Analysis ◽  
Manufacturing Enterprises ◽  
Equipment Manufacturing ◽  
Equipment Manufacturing Industry

Equipment manufacturing industry is the core industry of national economy. The development of artificial intelligence technology provides new development opportunities for the transformation and upgrading of equipment manufacturing industry, but in this process, China’s equipment manufacturing enterprises are faced with serious financing constraints and financing efficiency needs to be improved. Based on the panel data of Listed Companies in equipment manufacturing industry from 2009 to 2018, the article constructs a panel data regression model by using stochastic frontier analysis to measure the financing efficiency of equipment manufacturing industry and study its influencing factors. The results show that the average financing efficiency of China’s equipment manufacturing enterprises is in the medium level, while the traditional equipment manufacturing industry is lower; external financing has a positive impact on the financing efficiency of enterprises, and labor input has a negative impact; in the analysis of influencing factors, the Capital structure, R&D investment, Accounts receivable turnover rate, Fixed assets turnover rate have a great impact on the financing efficiency. The research results have a certain reference significance for equipment manufacturing enterprises to improve financing efficiency.

A Novel Knowledge Diffusion Efficiency Prediction Arithmetic in Equipment Manufacturing Industry Based on Simulated Annealing Arithmetic

2013 ◽  
Vol 441 ◽  
pp. 768-771
Author(s):  
Xu Sheng Chen ◽  
Wen Jun Yue ◽  
Hong Qi Wang
Keyword(s):  
Simulated Annealing ◽  
Manufacturing Industry ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Nonlinear Function ◽  
Knowledge Diffusion ◽  
Related Data ◽  
Equipment Manufacturing ◽  
Equipment Manufacturing Industry ◽  
Arbitrary Precision

A novel knowledge diffusion efficiency prediction arithmetic in equipment manufacturing industry in China was proposed, Radial basis function neural network (RBFNN) was designed, and simulated annealing arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the precision with 10-4 error, also the learning speed is quick and predictions are ideal. Trainings have been done with other networks in comparison. Back-propagation learning algorithm network does not converge until 2000 iterative procedure, and exactness design RBFNN is time-consuming and has big error. The arithmetic can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

scholarly journals Attribute-oriented Classification with Variable Importance using Random Forest Model

2019 ◽  
Vol 8 (2S3) ◽  
pp. 1630-1635
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Large Data ◽  
Variable Importance ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Importance Measure ◽  
Variable Importance Measure

In the present century, various classification issues are raised with large data and most commonly used machine learning algorithms are failed in the classification process to get accurate results. Datamining techniques like ensemble, which is made up of individual classifiers for the classification process and to generate the new data as well. Random forest is one of the ensemble supervised machine learning technique and essentially used in numerous machine learning applications such as the classification of text and image data. It is popular since it collects more relevant features such as variable importance measure, Out-of-bag error etc. For the viable learning and classification of random forest, it is required to reduce the number of decision trees (Pruning) in the random forest. In this paper, we have presented systematic overview of random forest algorithm along with its application areas. In addition, we presented a brief review of machine learning algorithm proposed in the recent years. Animal classification is considered as an important problem and most of the recent studies are classifying the animals by taking the image dataset. But, very less work has been done on attribute-oriented animal classification and poses many challenges in the process of extracting the accurate features. We have taken a real-time dataset from the Kaggle to classify the animal by collecting the more relevant features with the help of variable importance measure metric and compared with the other popular machine learning models.

scholarly journals Deducing Optimal Classification Algorithm for Heterogeneous Fabric

2022 ◽  
Author(s):  
Omar Alfarisi ◽  
Zeyar Aung ◽  
Mohamed Sassi
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Learning Algorithm ◽  
Synthetic Data ◽  
Supervised Machine Learning ◽  
Machine Learning Algorithm ◽  
Data Set ◽  
Heterogeneous Rock ◽  
Optimal Classification ◽  
Optimal Machine

For defining the optimal machine learning algorithm, the decision was not easy for which we shall choose. To help future researchers, we describe in this paper the optimal among the best of the algorithms. We built a synthetic data set and performed the supervised machine learning runs for five different algorithms. For heterogeneous rock fabric, we identified Random Forest, among others, to be the appropriate algorithm.

Equipment Manufacturing Industry Knowledge Chain Efficiency Prediction Algorithm Based on Improved RBFNN

2013 ◽  
Vol 441 ◽  
pp. 776-779
Author(s):  
Xu Sheng Chen ◽  
Chen Peng Xu ◽  
Hong Qi Wang
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Manufacturing Industry ◽  
Nonlinear Function ◽  
Prediction Algorithm ◽  
Knowledge Chain ◽  
Equipment Manufacturing ◽  
Equipment Manufacturing Industry ◽  
Radial Basis

A new knowledge chain efficiency prediction arithmetic in equipment manufacturing industry in China was proposed, Radial basis function neural network (RBFNN) was designed, and initial temperature numerical calculation arithmetic was adopted to adjust the network weights. MATLAB program was compiled; experiments on related data have been done employing the program. All experiments have shown that the arithmetic can efficiently approach the precision with 10-4 error, also the learning speed is quick and predictions are ideal. Trainings have been done with other networks in comparison. Back-propagation learning algorithm network does not converge until 2400 iterative procedure, and Efficiency design Radial basis function neural network is time-consuming and has big error. The arithmetic in paper can approach nonlinear function by arbitrary precision, and also keep the network from getting into partial minimum.

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