Multi-mother wavelet neural network-based on genetic algorithm and multiresolution analysis for fast 3D mesh deformation

In order to accurately and conveniently identify the shearer running status, a novel approach based on the integration of rough sets (RS) and improved wavelet neural network (WNN) was proposed. The decision table of RS was discretized through genetic algorithm and the attribution reduction was realized by MIBARK algorithm to simply the samples of WNN. Furthermore, an improved particle swarm optimization algorithm was proposed to optimize the parameters of WNN and the flowchart of proposed approach was designed. Then, a simulation example was provided and some comparisons with other methods were carried out. The simulation results indicated that the proposed approach was feasible and outperforming others. Finally, an industrial application example of mining automation production was demonstrated to verify the effect of proposed system.

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Application of wavelet neural network model based on genetic algorithm in the prediction of high-speed railway settlement

10.1117/12.2222200 ◽

2015 ◽

Cited By ~ 1

Author(s):

Shihua Tang ◽

Feida Li ◽

Yintao Liu ◽

Lan Lan ◽

Conglin Zhou ◽

...

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Network Model ◽

Neural Network Model ◽

High Speed ◽

Wavelet Neural Network ◽

High Speed Railway ◽

Model Based

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The Coal Mine Flooding Emergency Ability Evaluation Based on GA-WNN

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.782 ◽

2012 ◽

Vol 452-453 ◽

pp. 782-788

Author(s):

Jin Feng Wang ◽

Li Jie Feng ◽

Zhao Hui Li

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Coal Mine ◽

Evaluation Model ◽

Wavelet Neural Network ◽

Time Frequency ◽

Model Based ◽

Time Frequency Localization ◽

Mine Flooding ◽

Ability Evaluation

For the coal resources working which are affected by the coal mine flooding seriously, this paper make an analysis on the factors which affect the coal mine flooding emergency ability evaluation model based on GA-WNN is established through the wavelet neural network value which is optimized with genetic algorithm. This model combined the global optimization ability of genetic algorithm with the time-frequency localization of wavelet neural network. This combination can make up for many defects (for example, the neural network structure should be given artificially, the function can got local minimum easily and so on). Therefore, the local mine flooding emergency ability evaluation model based on genetic algorithm and wavelet neural network have higher reliability and calculation ability, and is beneficial to the pre-control management for coal mine flooding rescue.

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A fault detection algorithm of wheel flats algorithm combined wavelet neural network with genetic algorithm

International Journal of Digital Content Technology and its Applications ◽

10.4156/jdcta.vol7.issue10.15 ◽

2013 ◽

Vol 7 (10) ◽

pp. 147-154

Author(s):

Gao Ruipeng ◽

Shang Chunyang ◽

Jiang Hang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Fault Detection ◽

Detection Algorithm ◽

Wavelet Neural Network

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A Methodology for Calculating Greenhouse Effect of Aircraft Cruise Using Genetic Algorithm-Optimized Wavelet Neural Network

Complexity ◽

10.1155/2020/7141320 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Yong Tian ◽

Lina Ma ◽

Songtao Yang ◽

Qian Wang

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Performance Evaluation ◽

Greenhouse Effect ◽

Network Architecture ◽

Wavelet Neural Network ◽

Civil Aviation ◽

International Airport ◽

Fuel Flow ◽

Reliable Assessment

Reliable assessment on the environmental impact of aircraft operation is vital for the performance evaluation and sustainable development of civil aviation. A new methodology for calculating the greenhouse effect of aircraft cruise is proposed in this paper. With respect to both cruise strategies and wind factors, a genetic algorithm-optimized wavelet neural network topology is designed to model the fuel flow-rate and developed using the real flight records data. Validation tests demonstrate that the proposed model with preferred network architecture can outperform others investigated in this paper in terms of accuracy and stability. Numerical examples are illustrated using 9 flights from Beijing Capital International Airport to Shanghai Hongqiao International Airport operated by Boeing 737–800 aircraft on October 2, 2019, and the generated fuel consumption, CO2 and NOx emissions as well as temperature change for different time horizons can be effectively given through the proposed methodology, which helps in the environmental performance evaluation and future trajectory planning for aircraft cruise.

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