Study on the application of BP neural network optimized based on various optimization algorithms in storm surge prediction

Author(s):  
Xiao-qi Zhang ◽  
Si-qi Jiang

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.

2014 ◽  
Vol 543-547 ◽  
pp. 2084-2088 ◽  
Author(s):  
Run Biao Bao ◽  
Man Zhang

To reduce the prediction error rate of earthquake casualties, the paper proposed a prediction model with two steps: (1) screening of the earthquake casualties correlation factors; (2) improving the predictive veracity of general BP(Back Propagation) neural network model.By the analysis of 9 kinds of correlation factors, the paper established the MIV(Mean Impact Value) model based on BP neural network to screen the final correlation factors, and the paper got 6 main correlation factors according to the size of output weights of the factors. Finally, the paper verified the accuracy and practicability of the model through the validation of the model and the solving of prediction error of relevant factors hasn't been selected.


2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods


2013 ◽  
Vol 333-335 ◽  
pp. 1384-1387
Author(s):  
Jin Jie Yao ◽  
Xiang Ju ◽  
Li Ming Wang ◽  
Jin Xiao Pan ◽  
Yan Han

Target localization technology has been intensively studied and broadly applied in many fields. This paper presents one improved particle swarm optimization technique in training a back-propagation neural network for position estimation in target localization. The proposed scheme combines particle swarm optimization (PSO), back-propagation neural network (BP), adaptive inertia weight and hybrid mutation, called IPSO-BP. To verify the proposed IPSO-BP approach, comparisons between the PSO-based BP approach (PSO-BP) and general back-propagation neural network (BP) are made. The computational results show that the proposed IPSO-BP approach exhibits much better performance in the training process and better prediction ability in the validation process than those using the other two base line approaches.


2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss


2013 ◽  
Vol 333-335 ◽  
pp. 2469-2474
Author(s):  
Fei Guo ◽  
Xiao Luo

In order to meet the requirements of real-time and embedded of industrial field, a reconfigurable Back-Propagation neural network based on FPGA has been implemented on Xilinx's Spartan-3E (XC3S250E) chip which has 250000 gate. First the optimal network structure and weights were gotten by a variable structure of BP neural network algorithm. Then an improved hardware approaching method of excitation function was put forward, and the maximum error was 1.58% by simulation and comparative analysis on the error. Finally hardware co-imitation and timing simulation was token based on a reasonable choice of data accuracy, and then the hardware BP neural network algorithm was been downloaded and implemented on FPGA. This method has better accuracy and speed, it is an effective method of BP neural network modeling based on hardware, and lays the foundation for the hardware realization of other neural network and embedded image processing.


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