Comparison of GA-BP and PSO-BP neural network models with initial BP model for rainfall-induced landslides risk assessment in regional scale: a case study in Sichuan, China

2019 ◽  
Vol 100 (1) ◽  
pp. 173-204 ◽  
Author(s):  
Chonghao Zhu ◽  
Jianjing Zhang ◽  
Yang Liu ◽  
Donghua Ma ◽  
Mengfang Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Risk Assessment ◽  
Bp Neural Network ◽  
Regional Scale ◽  
Network Models ◽  
Neural Network Models ◽  
Bp Model

Modified BP Neural Network Model is Used for Odd-Even Discrimination of Integer Number

2013 ◽  
Vol 423-426 ◽  
pp. 2675-2678 ◽  
Author(s):  
Bao Long Hu ◽  
Ji Ren Xu ◽  
Huai Hui Gao ◽  
Ji Hai Liu ◽  
Ke Ren Wang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Bp Algorithm ◽  
Integer Number ◽  
Neural Network Models ◽  
Bp Neural Network Model ◽  
Bp Model

This paper introduced the BP neural network model and the BP algorithm in detail, and pointed out the BP neural network existed the defects of local optimal tendency of local optimal, slowed convergence speed etc. Through the modified BP algorithm, we could solve the problems existing in the traditional BP algorithm successfully, simulation results for odd-even discrimination of integer number based on MATLAB BP algorithm show that modified BP model compared with BP model has faster training speed and high study accuracy. Modified BP neural network models is used in practice, as long as it is complementary with effective measures, and we can get satisfactory result completely.

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

Artificial Neural Network Models as a Decision Support Tool for Selection in Sugarcane: A Case Study Using Seedling Populations

Crop Science ◽  
2011 ◽  
Vol 51 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Marvellous M. Zhou ◽  
Collins A. Kimbeng ◽  
Thomas L. Tew ◽  
Kenneth A. Gravois ◽  
Michael J. Pontif
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Decision Support ◽  
Network Models ◽  
Decision Support Tool ◽  
Neural Network Models ◽  
Support Tool ◽  
Artificial Neural ◽  
Artificial Neural Network Models

Modeling and Prediction of Foreign Currency Exchange Markets

2006 ◽  
pp. 139-151
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul A. Sarker ◽  
Rezaul K. Begg
Keyword(s):  
Neural Network ◽  
Foreign Currency ◽  
Performance Metrics ◽  
Network Models ◽  
Research Direction ◽  
Global Market ◽  
Future Research ◽  
Neural Network Models ◽  
Currency Exchange

In today’s global market economy, currency exchange rates play a vital role in national economy of the trading nations. In this chapter, we present an overview of neural network-based forecasting models for foreign currency exchange (forex) rates. To demonstrate the suitability of neural network in forex forecasting, a case study on the forex rates of six different currencies against the Australian dollar is presented. We used three different learning algorithms in this case study, and a comparison based on several performance metrics and trading profitability is provided. Future research direction for enhancement of neural network models is also discussed.

scholarly journals The Performance Study on the Long-Span Bridge Involving the Wireless Sensor Network Technology in a Big Data Environment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13
Author(s):  
Liwen Zhang ◽  
Chao Zhang ◽  
Zhuo Sun ◽  
You Dong ◽  
Pu Wei
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Long Span ◽  
Long Span Bridge ◽  
Vibration Responses ◽  
Bp Neural Network Model

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

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