scholarly journals Predictions of Overbreak Blocks in Tunnels Based on the Wavelet Neural Network Method and the Geological Statistics Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Sun Shaorui ◽  
Liu Jiaming ◽  
Wei Jihong
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Field Survey ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Tunnel Construction ◽  
Geometrical Parameters ◽  
Tunnel Excavation ◽  
Network Method ◽  
Fortran Language

Predicting overbreak blocks is a valid way to protect constructors, safeties in the process of tunnel excavation. In this paper, a prediction method of the overbreak blocks in tunnels is developed in the frame of the wavelet neural network of geological statistics models. Geometrical parameters of structural plane are first obtained by field survey. Then, a statistical model can be deduced from the measured geometrical parameters on the basis of the geological statistics theory. Furthermore, the volumes and distribution of the overbreak blocks are calculated by the theory of wavelet neural network. Finally, the valid support measurements can be designed according to the prediction results for all overbreak blocks appeared in tunnel excavation, and the amount of overbreak blocks can also be predicted. The code with respect to the method has been developed by the fortran language. The method proposed in this paper has been used in a tunnel construction. The results show that there exists an approximate 10%~30% difference between the prediction and the real volume of overbreak blocks. Therefore, the method can be well used to predict the volumes distribution and the overbreak blocks, and the accordingly support measurements can be also given according to the prediction results.

scholarly journals Research on bus elastic departure interval based on Wavelet Neural Network

2021 ◽  
Vol 261 ◽  
pp. 03052
Author(s):  
Zhe Lv ◽  
Jiayu Zou ◽  
Zhongyu Zhao
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Economic Costs ◽  
Passenger Flow ◽  
Flow Prediction ◽  
Intelligent Optimization Algorithm ◽  
Neural Network Prediction ◽  
Network Prediction ◽  
Important Basis

In recent years, more and more people choose to travel by bus to save time and economic costs, but the problem of inaccurate bus arrival has become increasingly prominent. The reason is the lack of scientific planning of departure time. This paper takes the passenger flow as an important basis for departure interval, proposes a passenger flow prediction method based on wavelet neural network, and uses intelligent optimization algorithm to study the bus elastic departure interval. In this paper, the wavelet neural network prediction model and the elastic departure interval optimization model are established, and then the model is solved by substituting the data, and finally the theoretical optimal departure interval is obtained.

Health Assessment of Aircraft Based on Wavelet Neural Network

2013 ◽  
Vol 756-759 ◽  
pp. 4581-4585
Author(s):  
Chen Guang Zhao ◽  
Xu Ping Qi ◽  
Chen Ming Mi ◽  
Teng Fei Miao
Keyword(s):  
Neural Network ◽  
Health Assessment ◽  
Prediction Method ◽  
Wavelet Neural Network ◽  
Fault Prediction ◽  
Health State ◽  
Learning Speed ◽  
Concept Of Health ◽  
Heavy Duty Gas Turbine ◽  
The Mathematical Model

Health assessment is one of the key technology in the aircraft operating system. Aiming at the characteristic of aircraft structure, the aircraft fault prediction method based on data mining is presented in this paper. The concept of health assessment is introduced first, the wavelet neural network provide the mathematical model reflecting aircraft health state. The experiment results show that the health prediction applying wavelet neural network works well with high fidelity and real time. Focusing at a typical heavy-duty gas turbine, the critical information collected by the sensor is applied as the network input, then the wavelet neural network is constructed, the quick training and learning speed is proved. The results indicate proposed approach is promising for reliable diagnostics of aircraft.

An Efficient Algorithm for Power Prediction in PV Generation System

2020 ◽  
Vol 9 (2) ◽  
pp. 207-216
Author(s):  
Qais Alsafasfeh
Keyword(s):  
Neural Network ◽  
Prediction Method ◽  
Weather Conditions ◽  
Cuckoo Search ◽  
Wavelet Neural Network ◽  
Mean Values ◽  
Neural Network Algorithm ◽  
The Mean ◽  
Pv Generation ◽  
Error Indicators

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. ©2020. CBIORE-IJRED. All rights reserved

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