scholarly journals A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B-Spline Curve with Knot Insertion Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Wenjuan Wang ◽  
Hongchun Yuan
Keyword(s):  
Neural Network ◽  
Storm Surge ◽  
Bp Neural Network ◽  
Knot Insertion ◽  
Tidal Level ◽  
B Spline ◽  
Spline Curve ◽  
Tide Level ◽  
Insertion Algorithm ◽  
Prediction Approach

Tide levels depend on both long-term astronomical effects that are mainly affected by moon and sun and short-term meteorological effects generated by severe weather conditions like storm surge. Storm surge caused by typhoons will impose serious security risks and threats on the coastal residents’ safety in production, property, and life. Due to the challenges of nonperiodic and incontinuous tidal level record data and the influence of multimeteorological factors, the existing methods cannot predict the tide levels affected by typhoons precisely. This paper targets to explore a more advanced method for forecasting the tide levels of storm surge caused by typhoons. First, on the basis of successive five-year tide level and typhoon data at Luchaogang, China, a BP neural network model is developed using six parameters of typhoons as input parameters and the relevant tide level data as output parameters. Then, for an improved forecasting accuracy, cubic B-spline curve with knot insertion algorithm is combined with the BP model to conduct smooth processing of the predicted points and thus the smoothed prediction curve of tidal level has been obtained. By using the data of the fifth year as the testing sample, the predicted results by the two methods are compared. The experimental results have shown that the latter approach has higher accuracy in forecasting tidal level of storm surge caused by typhoons, and the combined prediction approach provides a powerful tool for defending and reducing storm surge disaster.

Approximate Merging B-Spline Curves via Least Square Approximation

2014 ◽  
Vol 556-562 ◽  
pp. 3496-3500 ◽  
Author(s):  
Si Hui Shu ◽  
Zi Zhi Lin
Keyword(s):  
Approximation Error ◽  
Least Square ◽  
Error Tolerance ◽  
Knot Insertion ◽  
Least Squares Approximation ◽  
B Spline ◽  
Spline Curve ◽  
Spline Curves ◽  
Insertion Algorithm ◽  
Approximate Merging

An algorithm of B-spline curve approximate merging of two adjacent B-spline curves is presented in this paper. In this algorithm, the approximation error between two curves is computed using norm which is known as best least square approximation. We develop a method based on weighed and constrained least squares approximation, which adds a weight function in object function to reduce error of merging. The knot insertion algorithm is also developed to meet the error tolerance.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

Another knot insertion algorithm for B-spline curves

1992 ◽  
Vol 9 (3) ◽  
pp. 175-183 ◽  
Author(s):  
Phillip J. Barry ◽  
Rui-Feng Zhu
Keyword(s):  
Knot Insertion ◽  
B Spline ◽  
Spline Curves ◽  
Insertion Algorithm

scholarly journals A Stock Prices Prediction Approach Via Neural Network by Several Investor Indicators

2019 ◽  
pp. 477-484
Author(s):  
Yahui Chen ◽  
Zhan Wen ◽  
Qi Li ◽  
Yuwen Pan ◽  
Xia Zu ◽  
...  
Keyword(s):  
Neural Network ◽  
Stock Market ◽  
Bp Neural Network ◽  
Stock Prices ◽  
Stock Price ◽  
Quantitative Data ◽  
Prediction Models ◽  
Investor Behavior ◽  
Prediction Approach ◽  
The Impact

The prediction of stock indicators such as prices, trends and market indices is the focus of researchers. However, stock market has the characteristics of high noise and non-linearity. Generally, linear algorithms are not good for predicting stock market indicators. Therefore, BP neural network, a model suitable for nonlinear task, is widely used in stock market forecasting. However, many BP neural network prediction models are only based on historical stock quantitative data, and do not consider the impact of investor behavior on the stock market. Therefore, based on historical stock data and quantitative data of investor behavior of ten selected Chinese stocks, this paper trains a three-layer BP neural network to predict the stock prices such as the highest price ,the opening price ,the closing price, the lowest price in a short term. And then, the model that incorporates the investor behavior indicator is compared with the model that is not added. The results show that investor behavior indicators can improve the accuracy and generalization of the stock price forecasting model effectively, especially when the model based on stock quantitative data has a poor prediction accuracy on the test set.

Using Neural Networks Forecast the Economic Losses Caused by Storm Surge

2013 ◽  
Vol 798-799 ◽  
pp. 987-991
Author(s):  
Ling Di Zhao ◽  
Ming Ye Yang ◽  
Chun Peng Bian ◽  
Qing Hao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Storm Surge ◽  
Bp Neural Network ◽  
Storm Surges ◽  
Economic Loss ◽  
Entropy Method ◽  
Economic Conditions ◽  
Economic Losses ◽  
Grade Prediction

In order to make up for the lack of natural grade warning, we sought a new method for judging the losses of storm surges. Firstly apply entropy method etc to grade storm surges into 4 levels (mild, moderate, heavy and extra heavy) according to economic loss indices in Zhejiang Province. Then develop BP neural network to forecast losses with the selected indicators of natural, social and economic conditions. Comparing forecast grades with the actual value, we found the accuracy of grade prediction is 80%. It shown the grading results and predicting method are reliable and could be used for the grades of economic losses forecast of storm surges in future.

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

2022 ◽  
pp. 147509022110695
Author(s):  
Xiao-qi Zhang ◽  
Si-qi Jiang
Keyword(s):  
Neural Network ◽  
Storm Surge ◽  
Bp Neural Network ◽  
Prediction Error ◽  
Optimization Algorithms ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Optimal Model ◽  
Swarm Optimization ◽  
Error Method

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.

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