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.

An Efficiency Algorithm for SDCP Program

2014 ◽  
Vol 971-973 ◽  
pp. 1533-1536
Author(s):  
Ning Xiao
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Bp Neural Network ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Hybrid Intelligent Algorithm ◽  
Random Simulation ◽  
Training Samples ◽  
Stochastic Particle

For more effectively solving SDCP,in the paper,using BP neural networks to approximate chance function,training samples are produced by random simulation,and a hybrid intelligent algorithm for SDCP combined stochastic particle swarm optimization and BP neural network is proposed.The experimental results show that the algorithm is more preferable.

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.

Water-Inflow Forecast of Submarine Tunnel Based on BP Neural Networks

2011 ◽  
Vol 90-93 ◽  
pp. 2173-2177
Author(s):  
Chen Cai ◽  
Tao Huang ◽  
Xun Li ◽  
Yun Zhen Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Water Inflow ◽  
Water Volume ◽  
Network Algorithm ◽  
Forecast Precision ◽  
Neural Network Algorithm ◽  
Computation Analysis

The submarine tunnel water-inflow question has many kinds of factor synthesis influences, has highly the complexity and the misalignment, This article used the BP neural network algorithm to establish the submarine tunnel welling up water volume forecast model and to carry on the computation analysis, The result indicated that this model restraining performance is good, the forecast precision is high and simple feasible. This method has provided a new mentality for the submarine tunnel welling up water volume's forecast.

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.

scholarly journals Image recognition in the artificial agriculture officer

2018 ◽  
Vol 7 (3.3) ◽  
pp. 119
Author(s):  
B Lokesh ◽  
Ravoori Charishma ◽  
Natuva Hiranmai
Keyword(s):  
Neural Networks ◽  
Information Retrieval ◽  
Search Engine ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
Crop Production ◽  
Android Application ◽  
Weather Patterns ◽  
Crop Diseases ◽  
The Right

Farmers face a multitude of problems nowadays such as lower crop production, tumultuous weather patterns, and crop infections. All of these issues can be solved if they have access to the right information. The current methods of information retrieval, such as search engine lookup and talking to an Agriculture Officer, have multiple defects. A more suitable solution, that we are proposing, is an android application, available at all times, that can give succinct answers to any question a farmer may pose. The application will include an image recognition component that will be able to recognize a variety of crop diseases in the case that the farmer does not know what he is dealing with and is unable to describe it.  Image recognition is the ability of a computer to recognize and distinguish between different objects, and is actually a much harder problem to solve than it seems. We are using Tensorflow, a tool that uses convolutional neural networks, to implement it  

Intelligent Identification of Flow Regime Based on Improved BP and IMFs

2013 ◽  
Vol 816-817 ◽  
pp. 471-474
Author(s):  
Qiang Wang
Keyword(s):  
Neural Network ◽  
Flow Regime ◽  
Bp Neural Network ◽  
Flow Regimes ◽  
Characteristic Vector ◽  
Soft Sensing ◽  
Mode Decomposition ◽  
Training Samples ◽  
The Difference ◽  
Regime Characteristic

Aimed at the characteristic of nonlinear and non-stationary of pressure drop, in this article a flow regime identification soft sensing method using Hilbert-Huang transformation combined with improved BP neural network is put forward. The method analyzes the intrinsic mode function (IMFs) obtained after the empirical mode decomposition (EMD), then extracts IMF energy as the characteristic vector of an improved BP neural network with self-adapted learning ratio. Learning form training samples, the network could accomplish the objective identification of the unknown flow regimes. The simulated results showed that the flow regime characteristic vector which was obtained by IMFs could reflect the difference between various flow regimes and the recognition possibility of the network could reached up to about 95 percent. This study provided a new way to identify flow regime by soft sensing.

Application of Magnetorheological Damper Semi-Active Control Based on Artificial Neural Networks

2010 ◽  
Vol 171-172 ◽  
pp. 654-658
Author(s):  
De Kun Yue ◽  
Qi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
System Identification ◽  
Active Control ◽  
Bp Neural Network ◽  
Structural Response ◽  
Magnetorheological Damper ◽  
Building Structure ◽  
Artificial Neural

Uncertainty for the building structure and nonlinear, this simulation of a multi-storey structure under earthquake is presented based on the BP neural network and system identification, controller will be built to effectively reduce the structural response, and to strengthen the unique damper performance.

Application of Probabilistic Neural Network in Pattern Classification

2013 ◽  
Vol 441 ◽  
pp. 738-741 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang ◽  
Qing Hui Wu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Classification ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Networks ◽  
Training Samples ◽  
The One ◽  
Speed Accuracy

The network model of probabilistic neural network and its method of pattern classification and discrimination are first introduced in this paper. Then probabilistic neural network and three usually used back propagation neural networks are established through MATLAB7.0. The pattern classification of dots on a two-dimensional plane is taken as an example. Probabilistic neural network and improved back propagation neural networks are used to classify these dots respectively. Their classification results are compared with each other. The simulation results show that compared with back propagation neural networks, probabilistic neural network has simpler learning rules, faster training speed and it needs fewer training samples; the pattern classification method based on probabilistic neural network is very effective, and it is superior to the one based on back propagation neural networks in classifying speed, accuracy as well as generalization ability.

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