Expert System for Acidizing Based on BP Neural Network

2012 ◽  
Vol 548 ◽  
pp. 438-443
Author(s):  
Heng Xue ◽  
Ping Li Liu ◽  
Nian Yin Li ◽  
Zhi Feng Luo ◽  
Li Qiang Zhao
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Bp Neural Network ◽  
Petroleum Industry ◽  
Field Application ◽  
Oil Field ◽  
Mathematical Technique ◽  
Huge Amount ◽  
The Neural Network ◽  
Training Samples

The technique of acidizing stimulation is one of the most critical measures in petroleum industry to enhance production. As acidizing technique being an integrated course which combines science, practice and experience in one, it cannot be explained by mathematical technique precisely. For conventional acidizing, the workload is extremely huge and complicated, since it has built an extensive database with the help of a huge amount of the application samples. The Neural Network has the generalization ability, which not only has the most consistency with training samples, but also is a dependable network for predication of test samples, whose data distribution is similar to the previous ones. Expert system for acidizing based on the BP Neural Networks can predict a favorable acidizing fluids system and suitable dosage reasonably, effectively and accurately with a large pool of initial input parameters. Thereby this expert system can assist field application and realize the systematization and intelligence in oil field.

Prediction and analysis of College Students’ mental health based on BP neural network

2020 ◽  
pp. 1-7
Author(s):  
Tang Yushou Su Jianhuan
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
College Students ◽  
Bp Neural Network ◽  
Structural Characteristics ◽  
Prediction System ◽  
The Neural Network ◽  
Data Processing Method ◽  
Training Samples ◽  
The Mathematical Model

College Students’ mental health is an important part of higher education, so the current research and prediction of College Students’ mental health are of great significance to better solve the problem of College Students’ mental health. Taking a local university as an example, the data from 2011 to 2019 are selected and analyzed. The normalized data processing method is used to assign weights to 11 kinds of factors that affect the health of college students. The training samples of a neural network are selected, and the structural characteristics of the neural network and the artificial neural network toolbox of MATLAB are used to establish the BP based model the mathematical model of the prediction system of College Students’ mental health based on neural network. The results show that the error between the predicted value and the measured value is only 0.88%. On this basis, this paper uses the model to predict the weight of the influencing factors of the mental health status of college students in a local university in 2020 and analyzes the causes of the prediction results, to provide the basis for the current mental health education of college students.

Image Segmentation of Eggplants in Growing Environment Based on Improved BP Neural Network

2010 ◽  
Vol 40-41 ◽  
pp. 599-603
Author(s):  
Jian Song
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Bp Neural Network ◽  
Image Features ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Algorithm ◽  
Growing Environment ◽  
Improved Bp Algorithm ◽  
Gray Values

Aim at the complex background of eggplant image in the growing environment, a image segmentation method based on BP neural network was put forward. The EXG gray values of 3×3 neighborhood pixels were obtained as image features through by analyzing the eggplant image. 30 eggplant images were taken as training samples and results of manual segmentation images by Photoshop were regarded as teacher signals. The improved BP algorithm was used to train the parameter of the neural network. The effective parameter was achieved after 120 times of training. The result of this experiment showed that the eggplant fruit could be preferably segmented from the background by using BP neural network algorithm and it could totally meet the demands of the picking robots after further processing by way of combining mathematics morphology with median filtering.

Research on Expert System of Crop Disasters Based on Neural Networks

2012 ◽  
Vol 235 ◽  
pp. 34-38
Author(s):  
Jie Zhu ◽  
Wei Dong ◽  
Jing Zhang ◽  
Xu Ning Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Expert System ◽  
Bp Neural Network ◽  
Crop Production ◽  
Weight Optimization ◽  
Training Samples ◽  
Crop Diseases ◽  
Crop Diagnosis ◽  
Adjustment Of Parameters

In order to improve the efficiency of prediction and diagnose of crop diseases and pests, and solve the problems during the course of crop production and management, then an neural networks with weight adjustment of prediction model is proposed. The crop disasters are regarded as example, after the symptoms of disasters and features are classified, abstracted and coded, the adjustment of weight, optimization of network structure and reasonable adjustment of parameters of BP neural network are discussed, then a model is constructed to forecast the disasters of crop, the weight is used as knowledge to predict disasters of crop through studying training samples. Results have shown that the optimization expert system of crop disasters based on neural network has enhanced the ability of decision making of expert system, then greatly improved the accuracy and reliability of crop diagnosis.

Pressure Prediction Technology of the Deep Strata Based on BP Neural Network

2010 ◽  
Vol 143-144 ◽  
pp. 28-31 ◽  
Author(s):  
Wei Li ◽  
Tie Yan ◽  
Ying Jie Liang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Gamma Ray ◽  
Forecast Model ◽  
Oil Field ◽  
The Neural Network ◽  
Natural Gamma ◽  
Gamma Ray Log ◽  
Deep Strata ◽  
Very High

. The accurate prediction of strata pressure is the base for safely, quality and efficiently drilling, decreasing hole problems and reasonable development of the reservoir. Because of the high cost, long cycle of the formation pressure measured method, which may influence the safety of drilling operation, thus a new method for predicting strata pressure, based on the BP neural network, is presented in this paper, and establishing process of the neural network forecast model are discussed in detail. This method takes the acoustic time, natural potential, natural gamma ray log data and pipe pressure test data as study sample, which has a very high accuracy. The paper predicts strata pressure of the Saertu oil field and Xingshugang oil field in Daqing, and the results show that relative error between the predicted data and experimental data is less than ±8.9%.

Application of BP Neural Network in the Reliability Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3814-3818 ◽  
Author(s):  
Wei Jiang ◽  
Meng Zhang ◽  
Zhi Ling Chen ◽  
Yun Liu ◽  
Ning Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Reliability Prediction ◽  
Reliability Parameters ◽  
The Neural Network ◽  
Training Samples ◽  
Neural Network Toolbox

Using neural network BP algorithm and the neural network toolbox of MATLAB, this paper presented a new reliability prediction model of the products. Its processes included that confirming training samples, putting up the network that was initialized, training the network and predicting reliability parameters of the products. At last reliability parameters of an example were predicted with the reliability prediction, the prediction effect was more perfect.

Building Electronic Commerce Recommendation System Based on Ontology Learning and BP Neural Network

2013 ◽  
Vol 718-720 ◽  
pp. 1961-1966
Author(s):  
Hong Sheng Xu ◽  
Qing Tan
Keyword(s):  
Neural Network ◽  
Electronic Commerce ◽  
Bp Neural Network ◽  
Optimization Problem ◽  
Recommendation System ◽  
High Efficiency ◽  
Ontology Learning ◽  
The Neural Network ◽  
Network Process ◽  
Hidden Layer

Electronic commerce recommendation system can effectively retain user, prevent users from erosion, and improve e-commerce system sales. BP neural network using iterative operation, solving the weights of the neural network and close values to corresponding network process of learning and memory, to join the hidden layer nodes of the optimization problem of adjustable parameters increase. Ontology learning is the use of machine learning and statistical techniques, with automatic or semi-automatic way, from the existing data resources and obtaining desired body. The paper presents building electronic commerce recommendation system based on ontology learning and BP neural network. Experimental results show that the proposed algorithm has high efficiency.

The Research on Recognition of the Irregular Objects

2012 ◽  
Vol 241-244 ◽  
pp. 1602-1607
Author(s):  
Guang Hai Han ◽  
Xin Jun Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Convergence Speed ◽  
Know How ◽  
The Neural Network ◽  
Variable Step ◽  
Adaptive Momentum ◽  
The Way

It usually need different ways to process different objects in the manufacturing, Therefore, firstly we need to distinguish the categories of objects to be processed, then the machine will know how to deal with the objects. In order to automatically recognize the category of the irregular object, this paper extracted the improved Hu's moments of each object as the feature by the way of processing images of the working platform that the irregular objects are putting on. This paper adopts the variable step BP neural network with adaptive momentum factor as the classifier. The experiment shows that this method can effectively distinguish different irregular objects, and during the training of the neural network, it has faster convergence speed and better approximation compared with the traditional BP neural network

GTAW Procedure Expert System Based on Neural Network

2013 ◽  
Vol 455 ◽  
pp. 425-430 ◽  
Author(s):  
Xue Wu Wang ◽  
Shang Yong Yang
Keyword(s):  
Neural Network ◽  
Expert System ◽  
Expert Knowledge ◽  
Welding Process ◽  
Learning Ability ◽  
Welding Parameters ◽  
Neural Network Learning ◽  
The Neural Network ◽  
Function Design ◽  
Welding Procedure

Intelligent procedure expert system was developed to select appropriate GTAW procedure in this paper. First, the function design and implementation methods of the welding procedure expert system were introduced. The expert system can present the welding procedure card, multimedia display of welding process, and output function to makes the data sharing more convenient. Then, the database design of the welding procedure expert system based on C/S mode was presented where the expert knowledge was stored. At last, the neural network model was established to realize procedure selection based on the neural network learning ability and the welding case from the database. With the BPNN model, the welding parameters can be obtained based on the input welding conditions.

scholarly journals Research on GDP Forecast Analysis Combining BP Neural Network and ARIMA Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shaobo Lu
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Error Correction ◽  
Relative Error ◽  
Bp Neural Network ◽  
Arima Model ◽  
Forecast Model ◽  
Price Prediction ◽  
The Neural Network

Based on the BP neural network and the ARIMA model, this paper predicts the nonlinear residual of GDP and adds the predicted values of the two models to obtain the final predicted value of the model. First, the focus is on the ARMA model in the univariate time series. However, in real life, forecasts are often affected by many factors, so the following introduces the ARIMAX model in the multivariate time series. In the prediction process, the network structure and various parameters of the neural network are not given in a systematic way, so the operation of the neural network is affected by many factors. Each forecasting method has its scope of application and also has its own weaknesses caused by the characteristics of its own model. Secondly, this paper proposes an effective combination method according to the GDP characteristics and builds an improved algorithm BP neural network price prediction model, the research on the combination of GDP prediction model is currently mostly focused on the weighted form, and this article proposes another combination, namely, error correction. According to the price characteristics, we determine the appropriate number of hidden layer nodes and build a BP neural network price prediction model based on the improved algorithm. Validation of examples shows that the error-corrected GDP forecast model is also better than the weighted GDP forecast model, which shows that error correction is also a better combination of forecasting methods. The forecast results of BP neural network have lower errors and monthly prices. The relative error of prediction is about 2.5%. Through comparison with the prediction results of the ARIMA model, in the daily price prediction, the relative error of the BP neural network prediction is 1.5%, which is lower than the relative error of the ARIMA model of 2%.

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