3-D Topology Optimization of Claw-Pole Alternator Using Gaussian-Basis Function With Global and Local Searches

Otomo, Yoshitsugu; Igarashi, Hajime; Hidaka, Yûki; Komatsu, Taiga; Yamada, Masaki

doi:10.1109/tmag.2019.2952205

Cited by 6 publications

(2 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature [ 10 ] studies the fractal characteristics of some time series, analyzes the R/S analysis in fractal theory, and uses R/S method to find the change rule from the time series with fractal characteristics to predict the future development trend of the series. Literature [ 11 ] proposes a similarity search algorithm based on dynamic time warping technology, which obtains the sequence matching by calculating the shortest warping path between time series data and compares the comprehensive control time series data by clustering analysis based on different distance measures, which greatly improves the calculation accuracy and has strong robustness to amplitude difference, noise and linear drift. Literature [ 12 ] uses the neural network to predict multifactor time series data, inputs two pieces of related information in the neural network at the same time, and then uses it to predict; the predicted value is much better than the previous regression analysis results.…”

Section: Related Workmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The prediction of gross domestic product (GDP) is a research hotspot, and its importance is self-evident. Its complex internal change mechanism also increases the difficulty of analyzing GDP data. The genetic algorithm (GA) is applied to the parameter design of the radial basis function neural network (RBFNN) based on genetic algorithm optimization (RBFNN-GA). An economic zone GDP image prediction model is proposed, which realizes the optimal design of the center vector, the base width vector of the RBFNN node function, and the weight between the hidden layer and output layer. Based on the GDP data over the years, this paper uses the RBFNN-GA prediction model to analyze and predict the GDP image and compares the image prediction results. The results show that the genetic algorithm is used to optimize RBFNN, which gives full play to the advantages of the two algorithms. The relative error of the RBFNN-GA prediction model is only 3.52%. Compared with the prediction results, the prediction accuracy is significantly higher than the ARIMA time series model and GM (1,1) model.

show abstract

Section: Related Workmentioning

confidence: 99%

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Zhu

Meng

2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…1) The first step coincides with the global search performed by the stochastic algorithm; 2) The configuration achieved in the first step is further optimized using methods involving sensitivity analysis. In [48], the local search is performed evolving the material boundary using the level-set equation, while in [97] an approach involving only the Normalized Gaussian network (NGnet) basis functions both for global and local search is proposed.…”

Section: ) Two-step Topology Optimization Methodsmentioning

confidence: 99%

Topology Optimization for Electromagnetics: A Survey

et al. 2022

View full text Add to dashboard Cite

The development of technologies for the additive manufacturing, in particular of metallic materials, is offering the possibility of producing parts with complex geometries. This opens up to the possibility of using topological optimization methods for the design of electromagnetic devices. Hence, a wide variety of approaches, originally developed for solid mechanics, have recently become attractive also in the field of electromagnetics. The general distinction between gradient-based and gradient-free methods drives the structure of the paper, with the latter becoming particularly attractive in the last years due to the concepts of artificial neural networks. The aim of this paper is twofold. On one hand, the paper aims at summarizing and describing the state-of-art on topology optimization techniques while on the other it aims at showing how the latter methodologies developed in non-electromagnetic framework (e.g., solid mechanics field) can be applied for the optimization of electromagnetic devices. Discussions and comparisons are both supported by theoretical aspects and numerical results.

show abstract

Circuit Coupled Simulation of Claw-Pole Machines: Comparison between Different Topologies

Omri¹,

Ibala²,

Ahmed³

2020

2020 6th IEEE International Energy Conference (ENERGYCon)

View full text Add to dashboard Cite

3-D Topology Optimization of Claw-Pole Alternator Using Gaussian-Basis Function With Global and Local Searches

Cited by 6 publications

References 9 publications

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

[Retracted] Macroeconomic Image Analysis and GDP Prediction Based on the Genetic Algorithm Radial Basis Function Neural Network (RBFNN‐GA)

Topology Optimization for Electromagnetics: A Survey

Circuit Coupled Simulation of Claw-Pole Machines: Comparison between Different Topologies

Contact Info

Product

Resources

About