Topology Optimization for a SynRM Rotor Using Normalized Gaussian Basis Function

2017 ◽  
Vol 2017.30 (0) ◽  
pp. 145
Author(s):  
Hidenori Sasaki ◽  
Hajime Igarashi
Keyword(s):  
Topology Optimization ◽  
Basis Function ◽  
Gaussian Basis Function

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

2020 ◽  
Vol 56 (1) ◽  
pp. 1-4
Author(s):  
Yoshitsugu Otomo ◽  
Hajime Igarashi ◽  
Yuki Hidaka ◽  
Taiga Komatsu ◽  
Masaki Yamada
Keyword(s):  
Topology Optimization ◽  
Basis Function ◽  
Gaussian Basis Function ◽  
Local Searches ◽  
Global And Local

Evolving Basis Function Networks for System Identification

2001 ◽  
Vol 5 (4) ◽  
pp. 229-238 ◽  
Author(s):  
Yuehui Chen ◽  
◽  
Shigeyasu Kawaji
Keyword(s):  
Basis Function ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Parameter Tuning ◽  
Unified Framework ◽  
B Spline ◽  
Gaussian Basis Function ◽  
Structure Adaptation ◽  
Random Search Algorithm

This paper is concerned with learning and optimization of different basis function networks in the aspect of structure adaptation and parameter tuning. Basis function networks include the Volterra polynomial, Gaussian radial, B-spline, fuzzy, recurrent fuzzy, and local Gaussian basis function networks. Based on creation and evolution of the type constrained sparse tree, a unified framework is constructed, in which structure adaptation and parameter adjustment of different basis function networks are addressed using a hybrid learning algorithm combining a modified probabilistic incremental program evolution (MPIPE) and random search algorithm. Simulation results for the identification of nonlinear systems show the feasibility and effectiveness of the proposed method.

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