Design of a Fan-Out Panel-Level SiC MOSFET Power Module Using Ant Colony Optimization-Back Propagation Neural Network

Qian, Yichen; Hou, Fengze; Fan, Jiajie; Lv, Quanya; Fan, Xuejun; Zhang, Kouchi

doi:10.1109/ted.2021.3077209

Cited by 14 publications

(17 citation statements)

References 25 publications

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“…See literature [18] for a definition of the particle swarm optimization algorithm. As an evolutionary computational technique, the optimal solution is obtained through collaboration between individuals in a population and information transfer between them, allowing the population solution space to be continuously updated.…”

Section: Improved Psomentioning

confidence: 99%

“…e prediction results of the proposed model were compared with the BP [11] model, LSTM [16] model, PCA + BP [18] model, GA + BP [23] model, and adaptive BP [24] model. e prediction results of the different evaluation models for some of the data are given in Table 1, and the absolute error comparison graph is shown in Figure 11.…”

Section: Model Comparison Experimentsmentioning

confidence: 99%

“…e authors of literature [16] proposed a teaching effectiveness evaluation method based on LSTM networks, which solved the problems such as the poor teaching effectiveness of traditional methods. To improve the performance of deep learning models, optimization algorithms are often used to optimize the models, such as ant colony algorithms and particle swarm algorithms [17,18]. e authors of literature [19] proposed a particle swarm optimization LSTM model for educational data.…”

Section: Introductionmentioning

confidence: 99%

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Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Yin

2022

International Transactions on Electrical Energy Systems

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Effective teaching behavior in physical education has an important impact on the quality of classroom teaching. To overcome the shortcomings in the existing university teaching quality assessment procedure, this paper designs an evaluation model for physical education departments based on long short-term memory networks (LSTM) with the improved particle swarm optimization (PSO). The model is constructed by analyzing the connotation and index system of teacher education teaching quality and constructing a fan-leaf structure model of teacher education teaching quality. Then, an improved PSO-LSTM model is proposed to train the teaching samples. The evaluation model applies the improved LSTM model and optimizes the network structure by dynamically adjusting the learning rate. The model is then optimized for the number of neurons and iterations of the network using the improved PSO. The results of the experiment indicate that the proposed model is effective in evaluating the quality of physical education. Moreover, the model analysis’ accuracy has greatly improved. This helps teachers have a comprehensive understanding of classroom dynamics and improve their professional competence and classroom teaching quality.

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Section: Improved Psomentioning

confidence: 99%

Section: Model Comparison Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Yin

2022

International Transactions on Electrical Energy Systems

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“…The first two algorithms release pheromones during construction, and the third one releases pheromones after completion [ 15 ]. The longest path traveled by ants is used as the overall information, and the pheromone strength is constant, which will affect the convergence speed.…”

Section: Methodsmentioning

confidence: 99%

Research on Urban National Sports Fitness Demand Prediction Method Based on Ant Colony Algorithm

Yue

Dai

2022

Computational Intelligence and Neuroscience

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With the development of competitive sports, the enthusiasm of the public to participate in sports has gradually increased. At present, almost all streets in the city have their own fitness places, which provide a lot of help for public fitness. However, the existing fitness venues are obviously insufficient, the venues are limited, relatively single, and the open-space area is insufficient, which cannot meet the needs of mass sports fitness. Based on this, this paper studies and analyzes the prediction of urban national sports fitness demand based on the ant colony algorithm. First, this paper analyzes the National Fitness Situation and the related research on demand forecasting and puts forward the use of the ant colony algorithm to realize demand forecasting. This paper expounds on the research methods and algorithms commonly used in demand forecasting. The ant colony algorithm is used to improve the fuzzy analysis. The urban national sports fitness demand is divided into six secondary indicators, and different tertiary indicators are divided under each secondary indicator. Through simulation analysis, it is confirmed that the improved algorithm proposed in this paper converges faster and finds the best path most. At the same time, the weight of the urban national sports fitness demand index is calculated.

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“…With the significant growth of social informatization, the theory and application of the BP neural network (BP) have made great progress and development and have a far-reaching impact on various fields. Many fields of comprehensive image analysis and evaluation have completed the development and reform of the traditional evaluation model by introducing the new idea of neural network [ 1 ]. In the traditional talent evaluation process, the evaluation results are closely related to the experience of evaluation experts.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing‐Optimized BP Neural Network

Zhao

Xu²,

Cheng

2021

Computational Intelligence and Neuroscience

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With the continuous development of social economy and the intensification of social competition, human resource management plays a more and more important role in the whole resource system. How to give full play to the advantages of human resources has become the key issue of human resource management evaluation. However, the current human resource management evaluation system has some problems, such as poor timeliness, one-sidedness, and subjectivity. Therefore, this paper proposes a BP image neural network optimized based on the simulated annealing algorithm to realize enterprise human resource management evaluation and image analysis. Through the learning of different time series samples, the average weight distribution scheme of main indicators is obtained, in which the average weight proportions of c1, c2, c3, and c4 are 25.5%, 24.8%, 17.9%, and 31.9%, respectively. In the comprehensive evaluation of enterprise employees, the error between the actual output and expected output is less than 4.5%. The results show that the BP image neural network based on simulated annealing algorithm has high accuracy in the image analysis and evaluation of enterprise human resource management. The output analysis results meet the actual needs of the enterprise and the personal development of employees and provide a decision-making scheme for the evaluation of enterprise human resource management.

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Design of a Fan-Out Panel-Level SiC MOSFET Power Module Using Ant Colony Optimization-Back Propagation Neural Network

Cited by 14 publications

References 25 publications

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Optimized LSTM Networks with Improved PSO for the Teaching Quality Evaluation Model of Physical Education

Research on Urban National Sports Fitness Demand Prediction Method Based on Ant Colony Algorithm

Evaluation and Image Analysis of Enterprise Human Resource Management Based on the Simulated Annealing‐Optimized BP Neural Network

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