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Application of thermal energy optimization based on improved neural network algorithm in green innovation capability evaluation of manufacturing enterprises

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2024年 55卷

作者： Li, Jun Li, Dongsheng Chongqing Univ Technol Sch Econ & Finance Chongqing 400054 Peoples R China

As the global focus on sustainable development continues to increase, manufacturing companies face the dual challenges of energy efficiency and environmental impact in the process of enhancing green innovation capabilities. The purpose of this study is to explore the application of improved neural network algorithm in thermal energy optimization, so as to improve the green innovation ability of manufacturing enterprises and promote their sustainable development. By constructing an improved neural network model and using big data analysis technology, this paper conducts in-depth analysis of thermal energy use in manufacturing enterprises, so as to identify and optimize energy consumption patterns. The study considered a variety of influencing factors, including equipment efficiency, production process and environmental policy, and evaluated the optimization effect through model training and testing. The experimental results show that the improved neural network algorithm can effectively identify thermal energy waste points, and put forward the corresponding optimization measures. After optimization, the energy use efficiency of manufacturing enterprises has been improved, carbon emissions have been significantly reduced, and the comprehensive evaluation score of green innovation ability has been improved, providing effective technical support for promoting the sustainable development of enterprises.

关键词： improved neural network algorithm Thermal energy optimization Manufacturing enterprises Evaluation of green innovation capability

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Application of data mining technology based on improved neural network algorithm in sports performance prediction

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SOFT COMPUTING 2023年 1-11页

作者： Duan, Hongyun Zhumadian Vocat & Tech Coll Publ Teaching Dept Zhumadian 463000 Henan Peoples R China

improved neural network algorithm is a subject involved in many fields at the same time. It is an important research direction in the modern popular artificial intelligence industry. Nowadays, it has been deeply loved by people, and it is also a category with the fastest development in the computer industry group. Therefore, this paper starts with the improvement of neural network algorithm, changes its local defects, and introduces the calculation form of firefly algorithm to optimize the calculation of improved neural network algorithm. In addition, combined with the form of data mining, the upgraded prediction model is established, and the relationship between physical education teaching methods and grades is predicted. The conclusions of this algorithm and model are successfully applied to future teaching. The results show that there is a positive correlation between physical education teaching methods and grades. Different teaching methods affect the change of grades. According to the trend of curve change, analyze the change process of grades, so as to understand students' learning status and adjust physical education teaching methods in time. This research makes the improved neural network algorithm have new research results in physical education and even in many fields of education. On the basis of this research, the mining form of educational data has been fully developed in the field of teaching, and its application in the future is also very broad.

关键词： improved neural network algorithm Data mining Physical education teaching Achievement prediction

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System simulation of computer image recognition technology application by using improved neural network algorithm

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SOFT COMPUTING 2023年第11期27卷 7639-7646页

作者： Wang, Xin Hulunbuir Univ Hulunbuir 021008 Inner Mongolia Peoples R China

Digital image technology is penetrating into various fields of people's life, and it has been very mature and can effectively store and transmit data. Moreover, there are still various researches on image recognition, the core of this technology. The algorithm is mainly based on computer technology to obtain the target image for different scene categories, thus completely replacing the traditional classification form. Because of the limitations of traditional identification technology, there are some problems in the actual use. It does not depend on the prior knowledge requirements and can carry out complex feature space division. In this paper, an image recognition computer system is established by introducing an improved neural network algorithm. The algorithm is designed and tested, and the results show it has lower image recognition error rate. Subsequently, this research result is applied to the actual scene for testing. The test results show that the improved neural network optimization algorithm can make the extracted features more accurately expressed in the image processing, which is more effective than the traditional algorithm.

关键词： improved neural network algorithm Computer Image recognition System application

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Artificial intelligence recruitment text automatic generation based on light detection and improved neural network algorithm

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Optical and Quantum Electronics 2024年第2期56卷 162-162页

作者： Huang, Xinbin Huang, Yu Mercado, Cecilia School of Electronic Information and Artificial Intelligence YiBin Vocational and Technical College Sichuan Yibin 644003 China School of Information Engineering Mianyang Teachers College Mianyang City 621000 China School of Advanced Studies Saint Louis University Baguio City 2600 Philippines

Traditional methods of automatic generation of recruitment text usually rely on a large number of data annotation and complex statistical algorithms, but these methods have certain limitations. In order to further optimize the processing power of neural network and improve its computational efficiency and stability, this paper aims to solve the shortcomings of convergence and local vulnerability in existing neural network algorithms, and make general improvements to them. Through optical detection and improved neural network algorithm, a more efficient method of automatic generation of artificial intelligence recruitment text is developed. Candidates' resumes are pre-processed with light detection technology to extract key information and filter out spam text. Then the processed text is input into the improved neural network model for training to improve the quality of recruitment text generation. The experimental results show that the proposed method has achieved significant improvement in the accuracy and readability of the automatic generation of recruitment texts, which can provide high quality recruitment texts for human resources departments, improve recruitment efficiency and reduce labor costs. This study provides new ideas and methods for further developing the application of artificial intelligence in the field of recruitment. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Artificial intelligence Automatic generation improved neural network algorithm Light detection Recruitment text

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Computer Communication network Fault Detection Based on improved neural network algorithm

ELECTRICA

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ELECTRICA 2022年第3期22卷 351-357页

作者： Sun, Dong Chopra, Pooja Bhola, Jyoti Neware, Rahul Henan Polytech Univ Sch Comp Sci & Technol Xinxiang Henan Peoples R China GNA Univ Phagwara Punjab India Natl Inst Technol Elect & Commun Engn Dept Hamirpur India Hogskulen Vestlandet Dept Comp Math & Phys Bergen Norway Lovely Profess Univ Sch Comp Applicat Jalandhar Punjab India

To detect computer communication network failures, a computer communication network fault detection based on an improved neural network algorithm is proposed. A network fault diagnosis example is used to verify the effectiveness of the method. There are many network failure phenomena. Here, the author selected 13 network fault information parameters for a comprehensive diagnosis of network failures. The author designed a three-layer backpropagation (BP) neural network. There are 13 nodes in the input layer, corresponding to the above 13 network fault parameter information. The output layer has three nodes, which output the fault code sequence. The training of the network uses the trainlm0 function. The performance function uses the mean square error performance function mse0 and set e = 0.001;the network learning rate is set to a = 0.05. The author selects 100 failure data as the training set for network training and selects 10 sets of samples as the test set. The experimental data shows that after the network has been trained 25 times, the output error reaches the set precision e. After training the BP network using this algorithm 140 times, the output error reaches the set precision e. This method effectively improves the effectiveness and accuracy of S network fault diagnosis.

关键词： network failure improved neural network algorithm BP neural network algorithm fault detection

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improved neural network 3D Space Obstacle Avoidance algorithm for Mobile Robot 12th

Improved Neural Network 3D Space Obstacle Avoidance Algorith...

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12th International Conference on Intelligent Robotics and Applications (ICIRA)

作者： Tong, Yuchuang Liu, Jinguo Liu, Yuwang Ju, Zhaojie Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Univ Portsmouth Sch Comp Portsmouth PO1 3HE Hants England

ISBN: (纸本)9783030275389;9783030275372

Path planning problems are classical optimization problems in many fields, such as computers, mathematics, transportation, robots, etc., which can be described as an optimization problem in mathematics. In this paper, the mathematical model of obstacle environment is established. The characteristics of neural network algorithm, simulated annealing algorithm and adaptive variable stepsize via linear reinforcement are studied respectively. A new neural network 3D space obstacle avoidance algorithm for mobile robot is proposed, which solves the problem of the computational duration and minimum distance of the traditional neural network obstacle avoidance algorithm in solving the optimal path. According to the characteristics of the improved neural network algorithm, it is fused with a variety of algorithms to obtain the optimal path algorithm that achieves the shortest path distance and meets the requirements of obstacle avoidance security. The simulation experiment of the algorithm is simulated by Matlab. The results show that the improved neural network spatial obstacle avoidance algorithm based on the multiple algorithms proposed in this paper can effectively accelerate the convergence speed of path planning, realize the minimum path distance, and achieve very good path planning effect.

关键词： Global path planning Obstacle avoidance algorithm improved neural network algorithm Adaptive variable stepsize Simulated annealing

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Analysis and Decision-making of Regional Economic Vitality and Its Influencing Factors

Analysis and Decision-making of Regional Economic Vitality a...

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作者： Jintao Fu Huilai Li The School of Electrical Engineering of Zhengzhou University

Since the reform and opening up,China's economy has developed ***,due to economic,social,natural,historical and other reasons,development has been uneven across regions,and regional economic variability also ***,an objective and comprehensive evaluation of regional economic vitality,and exploring the causes of such differences,are of great significance for achieving regional coordination in sustainable *** so-called economic vitality refers to the growth rate and potential of the total supply and demand in the economy of a city or province in a certain period of *** involves all aspects of GDP,the living environment of the enterprise,and the living standard of the *** to capital,labor,entrepreneurship,technological innovation,etc.,and efficiency in using these *** article takes Henan Province as the research object,similar to China's average economic development,establishes a reasonable analysis model,and analyzes the influencing factors of economic vitality changes from the perspective of population trends and corporate vitality trends.

关键词： improved neural network algorithm Factor Cluster Analysis Grey prediction model MATLAB

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