Aero-engine blade profile reconstruction based on adaptive step size bat algorithm and visualization of machining error

High-precision profile reconstruction is a key issue in the profile detection and visualization of aero-engine blades. A method based on adaptive step size bat algorithm (ASSBA) for blade profile reconstruction and an adaptive mesh model for visualization analysis of the key machining errors are proposed. Firstly, the original bat algorithm (BA) is improved to introduce the global stage and local search stage. Then, combined with the node layer characteristics of the blade measurement data, the ASSBA is used to fit the optimal surface. Further, the adaptive mesh is planned on the blade profile to extract various evaluation parameters. Finally, the algorithm analysis and verification are carried out based on a certain type of blade. The results show this reconstruction method can get the fitted surface more quickly and accurately than other iterative methods. Simultaneously, the visualization method and corresponding software system can intuitively visualize the blade profile error, the twist deformation error, the swept deformation error, the bending deformation error and the cross-section line profile error.

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Adaptive step-size natural gradient algorithm based on separating degree gradient

IET International Conference on Wireless Mobile and Multimedia Networks Proceedings (ICWMMN 2006) ◽

10.1049/cp:20061348 ◽

2006 ◽

Author(s):

Guangbiao Li ◽

Shimin Xu

Keyword(s):

Gradient Algorithm ◽

Natural Gradient ◽

Step Size ◽

Adaptive Step Size ◽

Adaptive Step

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Improved stochastic gradient adaptive filter with gradient adaptive step size

Electronics Letters ◽

10.1049/el:19980922 ◽

1998 ◽

Vol 34 (13) ◽

pp. 1300 ◽

Cited By ~ 2

Author(s):

Hong Chae Woo

Keyword(s):

Adaptive Filter ◽

Stochastic Gradient ◽

Step Size ◽

Adaptive Step Size ◽

Adaptive Step

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A novel auto‐tuned adaptive frequency and adaptive step‐size incremental conductance MPPT algorithm for photovoltaic system

International Transactions on Electrical Energy Systems ◽

10.1002/2050-7038.12813 ◽

2021 ◽

Author(s):

Jyotismita Mishra ◽

Subhadip Das ◽

Deepak Kumar ◽

Monalisa Pattnaik

Keyword(s):

Photovoltaic System ◽

Step Size ◽

Adaptive Step Size ◽

Incremental Conductance ◽

Adaptive Step

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In-flight range optimization of multicopters using multivariable extremum seeking with adaptive step size

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) ◽

10.1109/iros45743.2020.9340762 ◽

2020 ◽

Author(s):

Xiangyu Wu ◽

Mark W. Mueller

Keyword(s):

Extremum Seeking ◽

Step Size ◽

Adaptive Step Size ◽

Flight Range ◽

Adaptive Step

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An Adaptive Step-size Order Statistic Time Domain Equaliser for Discrete Multitone Systems

2007 IEEE International Symposium on Circuits and Systems ◽

10.1109/iscas.2007.378418 ◽

2007 ◽

Cited By ~ 5

Author(s):

Suchada Sitjongsataporn ◽

Peerapol Yuvapoositanon

Keyword(s):

Order Statistic ◽

Time Domain ◽

Step Size ◽

Adaptive Step Size ◽

Discrete Multitone ◽

Adaptive Step

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APPLICATION OF AN ADAPTIVE STEP-SIZE ALGORITHM IN MODELS OF HYPERINFLATION

Macroeconomic Dynamics ◽

10.1017/s136510051000088x ◽

2012 ◽

Vol 16 (S3) ◽

pp. 355-375 ◽

Cited By ~ 14

Author(s):

Olena Kostyshyna

Keyword(s):

New York ◽

Mean Squared Error ◽

American Economic Review ◽

Sufficient Information ◽

Recursive Algorithms ◽

Time Varying ◽

Step Size ◽

Adaptive Step Size ◽

Squared Error ◽

Adaptive Step

An adaptive step-size algorithm [Kushner and Yin,Stochastic Approximation and Recursive Algorithms and Applications, 2nd ed., New York: Springer-Verlag (2003)] is used to model time-varying learning, and its performance is illustrated in the environment of Marcet and Nicolini [American Economic Review93 (2003), 1476–1498]. The resulting model gives qualitatively similar results to those of Marcet and Nicolini, and performs quantitatively somewhat better, based on the criterion of mean squared error. The model generates increasing gain during hyperinflations and decreasing gain after hyperinflations end, which matches findings in the data. An agent using this model behaves cautiously when faced with sudden changes in policy, and is able to recognize a regime change after acquiring sufficient information.

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Fixed-point optimization of deep neural networks with adaptive step size retraining

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2017.7952347 ◽

2017 ◽

Cited By ~ 9

Author(s):

Sungho Shin ◽

Yoonho Boo ◽

Wonyong Sung

Keyword(s):

Neural Networks ◽

Fixed Point ◽

Deep Neural Networks ◽

Step Size ◽

Adaptive Step Size ◽

Adaptive Step

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An Adaptive Invasive Weed Optimization Algorithm

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001415590041 ◽

2015 ◽

Vol 29 (02) ◽

pp. 1559004 ◽

Cited By ~ 7

Author(s):

Shuo Peng ◽

A.-J. Ouyang ◽

Jeff Jun Zhang

Keyword(s):

Optimization Algorithm ◽

Initial Step ◽

Local Extremum ◽

Invasive Weed Optimization ◽

Step Size ◽

Invasive Weed ◽

Adaptive Step Size ◽

Initial Step Size ◽

Adaptive Step ◽

Optimization Search

With regards to the low search accuracy of the basic invasive weed optimization algorithm which is easy to get into local extremum, this paper proposes an adaptive invasive weed optimization (AIWO) algorithm. The algorithm sets the initial step size and the final step size as the adaptive step size to guide the global search of the algorithm, and it is applied to 20 famous benchmark functions for a test, the results of which show that the AIWO algorithm owns better global optimization search capacity, faster convergence speed and higher computation accuracy compared with other advanced algorithms.

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