Optimal Design of Adaptive Wavelet Neural Network Based on Hierarchy Genetic Algorithm

2013 ◽  
Vol 339 ◽  
pp. 301-306 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Jian Guo Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Optimal Design ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Adaptive Wavelet ◽  
Structure Problem ◽  
Standard Genetic Algorithm ◽  
Training Efficiency

Based on the study of adaptive wavelet neural networks, a hierarchy genetic algorithm is proposed to training network. Compared with standard genetic algorithm, the method can not only optimize network parameters such as scale factor, transform factor parameter and connection weights, but also solve structure problem of adaptive wavelet neural network. The result of simulation indicates that the algorithm can efficiently determinate the parameter and structure of adaptive wavelet neural network, and has better higher training efficiency and forecasting precision.

Modeling of coal consumption rate based on wavelet neural network

2017 ◽  
Vol 08 (03) ◽  
pp. 1750023
Author(s):  
Xiaoqiang Wen ◽  
Shuguang Jian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Consumption Rate ◽  
Orthogonal Experiment ◽  
Thermal Power ◽  
Morlet Wavelet ◽  
Wavelet Neural Network ◽  
Wavelet Function ◽  
Wavelet Neural Networks ◽  
Coal Consumption

In this paper, two wavelet neural network (WNN) frames which depend on Morlet wavelet function and Gaussian wavelet function were established. In order to improve the efficiency of model training, the momentum term was applied to modify the weights and thresholds, and the output of the network was summed up by function transformation of output layer nodes. When the Gaussian Wavelet Neural Networks (GWNN) and Morlet Wavelet Neural Networks (MWNN) were applied to coal consumption rate (CCR) estimation in a thermal power plant, the results confirmed their potency in function approximation. In addition, the influence of learning rate on the models was also discussed through the orthogonal experiment.

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

scholarly journals Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chuanan Yao ◽  
Xiankun Gao ◽  
Yongchang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Vital Role ◽  
Wavelet Neural Network ◽  
System Stability ◽  
Wavelet Neural Networks ◽  
Grid Security ◽  
The Third ◽  
Combined Models

Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

scholarly journals Multidimensional wavelet neural networks Based on polynomial powers of sigmoid

DAT Journal ◽  
2016 ◽  
Vol 1 (2) ◽  
pp. 106-123
Author(s):  
João Fernando Marar ◽  
Aron Bordin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Activation Function ◽  
Wavelet Neural Network ◽  
Back Propagation Algorithm ◽  
Wavelet Neural Networks ◽  
Low Dimensionality ◽  
Feed Forward Neural Networks ◽  
Sigmoid Functions

Wavelet functions have been used as the activation function in feed forward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical back propagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As examples of applications for the method proposed a framework for face verfication is presented.

scholarly journals Nonlinear Time-Series Analysis of Pulsation of Post-AGB Stars by Genetic Algorithm/Neural Network Hybrid Systems

2000 ◽  
Vol 176 ◽  
pp. 135-136
Author(s):  
Toshiki Aikawa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid System ◽  
Nonlinear Time Series ◽  
Photometric Data ◽  
Nonlinear Time Series Analysis ◽  
Agb Stars

AbstractSome pulsating post-AGB stars have been observed with an Automatic Photometry Telescope (APT) and a considerable amount of precise photometric data has been accumulated for these stars. The datasets, however, are still sparse, and this is a problem for applying nonlinear time series: for instance, modeling of attractors by the artificial neural networks (NN) to the datasets. We propose the optimization of data interpolations with the genetic algorithm (GA) and the hybrid system combined with NN. We apply this system to the Mackey–Glass equation, and attempt an analysis of the photometric data of post-AGB variables.

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.

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