Research on Railway Engineering Cost Prediction Model Based on Chaotic Neural Networks and CS

2011 ◽  
Vol 204-210 ◽  
pp. 1291-1294
Author(s):  
Yan Chun Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Influence Factors ◽  
Chaotic Neural Network ◽  
Short Term ◽  
Chaotic Neural Networks ◽  
Improve Accuracy ◽  
Railway Engineering ◽  
The Cost ◽  
Engineering Cost

It is always hard to draw on the experience of completed projects to predict engineering cost, and the nonlinear characteristic of the influence factors of engineering cost increases the difficulty of prediction. Less efforts and higher accuracy are the objects pursued by related researchers. In this paper, the Cost Significant theorem is applied to simplify computing and the chaotic neural network is used to improve accuracy. The prediction model is rooted from the nonlinear dynamic chaotic system theory and two techniques employed are phase space reconstruction and chaotic neural network construction. The experiment results indicate that the model is suitable for estimating short-term engineering investment and the prediction accuracy is improved.

scholarly journals Prediction Model of Weekly Retail Price for Eggs Based on Chaotic Neural Network

2013 ◽  
Vol 12 (12) ◽  
pp. 2292-2299 ◽  
Author(s):  
Zhe-min LI ◽  
Li-guo CUI ◽  
Shi-wei XU ◽  
Ling-yun WENG ◽  
Xiao-xia DONG ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Retail Price ◽  
Chaotic Neural Network

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

scholarly journals Combined Optimization Prediction Model of Regional Wind Power Based on Convolution Neural Network and Similar Days

Machines ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 80
Author(s):  
Yalong Li ◽  
Fan Yang ◽  
Wenting Zha ◽  
Licheng Yan
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Pearson Correlation ◽  
Combination Method ◽  
Random Fluctuation ◽  
Short Term ◽  
Data Set ◽  
Model Based

With the continuous optimization of energy structures, wind power generation has become the dominant new energy source. The strong random fluctuation of natural wind will bring challenges to power system dispatching, so it is necessary to predict wind power. In order to improve the short-term prediction accuracy of regional wind power, this paper proposes a new combination prediction model based on convolutional neural network (CNN) and similar days analysis. Firstly, the least square fitting and batch normalization (BN) are used to preprocess the data, and then the recent historical wind power data set for CNN is established. Secondly, the Pearson correlation coefficient and cosine similarity combination method are utilized to find similar days in the long-term data set, and the prediction model based on similar days is constructed by the weighting method. Finally, based on the particle swarm optimization (PSO) method, a combined forecasting model is established. The results show that the combined model can accurately predict the future short-term wind power curve, and the prediction accuracy is improved to different extents compared to a single method.

Predicting the Performance of High-Speed Railway Bridge Using Regression Neural Network Approach

2017 ◽  
Vol 873 ◽  
pp. 220-224 ◽  
Author(s):  
Young Chan Kim ◽  
Mosbeh R. Kaloop ◽  
Jong Wan Hu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
High Speed ◽  
Short Term ◽  
Good Tool ◽  
Neural Network Approach ◽  
High Speed Railway ◽  
Railway Bridges ◽  
Bridge Girder ◽  
Artificial Neural Network Ann

The performance prediction of High-speed railway bridges (HSRB) is vital to detect the behavior of bridges under different train’s speeds. This study aims to design a prediction model using the artificial neural network (ANN) to assess the performance of Yonjung high-speed bridge. A short-term health monitoring system is used to collect the behavior of bridge with different high-speed train’s speeds. The statistical analysis is utilized to evaluate the bridge under speeds 165 to 403 Km/h. The evaluation of bridge and prediction model showing that the bridge is safe, and the ANN is shown a good tool can be used to estimate a prediction model for the displacement of bridge girder.

scholarly journals Cost Index Predictions for Construction Engineering Based on LSTM Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Dong ◽  
Yuan Chen ◽  
Gang Guan
Keyword(s):  
Neural Network ◽  
Support Vector ◽  
Short Term ◽  
Cost Index ◽  
Long Distance ◽  
Research Knowledge ◽  
Construction Engineering ◽  
Engineering Cost ◽  
Optimization Mechanism

In recent years, the cost index predictions of construction engineering projects are becoming important research topics in the field of construction management. Previous methods have limitations in reasonably reflecting the timeliness of engineering cost indexes. The recurrent neural network (RNN) belongs to a time series network, and the purpose of timeliness transfer calculation is achieved through the weight sharing of time steps. The long-term and short-term memory neural network (LSTM NN) solves the RNN limitations of the gradient vanishing and the inability to address long-term dependence under the premise of having the above advantages. The present study proposed a new framework based on LSTM, so as to explore the applicability and optimization mechanism of the algorithm in the field of cost indexes prediction. A survey was conducted in Shenzhen, China, where a total of 143 data samples were collected based on the index set for the corresponding time interval from May 2007 to March 2019. A prediction framework based on the LSTM model, which was trained by using these collected data, was established for the purpose of cost index predictions and test. The testing results showed that the proposed LSTM framework had obvious advantages in prediction because of the ability of processing high-dimensional feature vectors and the capability of selectively recording historical information. Compared with other advanced cost prediction methods, such as Support Vector Machine (SVM), this framework has advantages such as being able to capture long-distance dependent information and can provide short-term predictions of engineering cost indexes both effectively and accurately. This research extended current algorithm tools that can be used to forecast cost indexes and evaluated the optimization mechanism of the algorithm in order to improve the efficiency and accuracy of prediction, which have not been explored in current research knowledge.

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