Wavelet Neural Network Based on Chaos Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 307-312 ◽  
Author(s):  
Hu Cheng Zhao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Optimal Parameters ◽  
Chaos Optimization ◽  
Chaos Optimization Algorithm ◽  
Learning Speed ◽  
Chaos Genetic Algorithm ◽  
Remarkable Progress

To improve the performance of Wavelet Neural Network (WNN), a hybrid WNN learning algorithm, which is combination of Genetic Algorithm (GA) and Chaos Optimization Algorithm (COA) in a mutual complementarity manner, is proposed. In the algorithm, GA is first used to roughly search the optimal parameters of WNN as a whole, and then COA is adopted to perform the refined search on the basis of the result obtained by GA, which can make remarkable progress in modeling accuracy, learning speed, and overcoming local convergence or precocity. Simulation show its effectiveness.

Wavelet Neural Network Optimization Based on Hybrid Hierarchy Genetic Algorithm

2012 ◽  
Vol 433-440 ◽  
pp. 823-828 ◽  
Author(s):  
Xiao Bo Yang ◽  
Ji Ning Feng ◽  
Zhe Jun Diao ◽  
Hong Yun Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Algorithm ◽  
Network Optimization ◽  
Geometrical Structure ◽  
Wavelet Neural Network ◽  
Bp Algorithm ◽  
Training Algorithm ◽  
Neural Network Optimization ◽  
Learning Speed

Based on studying wavelet neural network (WNN) training algorithm and geometrical structure, a new WNN optimization algorithm-hybrid hierarchy genetic is introduced. The algorithm is combined by hierarchy genetic algorithm and linear multi-regress. Hybrid hierarchy genetic algorithm (HHGA) can determine the structure and parameters of WNN from data at one time. The method has the merits of faster learning speed, higher precision. It is compared with traditional BP algorithm in this paper. The effectiveness of the algorithm is demonstrated.

A novel identification approach for shearer running status through integration of rough sets and improved wavelet neural network

2016 ◽  
Vol 230 (16) ◽  
pp. 2792-2805 ◽  
Author(s):  
Lei Si ◽  
Zhongbin Wang ◽  
Xinhua Liu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Rough Sets ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Decision Table ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Identification Approach ◽  
Simulation Results

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.

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

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.

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

Complexity ◽  
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.

scholarly journals Research on improved wavelet convolutional wavelet neural networks

2020 ◽  
Author(s):  
Jing-Wei Liu ◽  
Fang-Ling Zuo ◽  
Ying-Xiao Guo ◽  
Tian-Yue Li ◽  
Jia-Ming Chen
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
High Performance ◽  
Learning Algorithm ◽  
Wavelet Neural Network ◽  
Deep Learning Algorithm ◽  
Main Work ◽  
Good Ability ◽  
Fully Connected

AbstractConvolutional neural network (CNN) is recognized as state of the art of deep learning algorithm, which has a good ability on the image classification and recognition. The problems of CNN are as follows: the precision, accuracy and efficiency of CNN are expected to be improved to satisfy the requirements of high performance. The main work is as follows: Firstly, wavelet convolutional neural network (wCNN) is proposed, where wavelet transform function is added to the convolutional layers of CNN. Secondly, wavelet convolutional wavelet neural network (wCwNN) is proposed, where fully connected neural network (FCNN) of wCNN and CNN are replaced by wavelet neural network (wNN). Thirdly, image classification experiments using CNN, wCNN and wCwNN algorithms, and comparison analysis are implemented with MNIST dataset. The effect of the improved methods are as follows: (1) Both precision and accuracy are improved. (2) The mean square error and the rate of error are reduced. (3) The complexitie of the improved algorithms is increased.

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