scholarly journals Wolf is Coming— Dynamic Classification Prediction Model of Vespa Mandarinia

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Yang Yue ◽  
Haomiao Niu ◽  
Jiao Liu
Keyword(s):  
Neural Network ◽  
Cellular Automata ◽  
Prediction Model ◽  
Diffusion Model ◽  
Classification Accuracy ◽  
Propagation Process ◽  
The Neural Network ◽  
Natural Domain ◽  
Classification Prediction ◽  
Set Up

Given the threat of Vespa mandarinia invasion to ecological balance, according to the data and information provided, the dynamic reproduction model of Vespa mandarinia is established by using natural domain interpolation, and the variation law of total bumblebee with time, latitude, and longitude is obtained. At the same time, we established theclassification prediction model by using a neural network and established the mapping relationship between time and space to evaluation grade.we meshed the area provided by the title, assigned values to the location of Vespa mandarinia(VM), and established a VM diffusion model with natural neighborhood interpolation. Its propagation process is simulated by cellular automata. It is determined that VM spreads in a circular shape centered at (122.93174°W, 48.93457°N) and (122.57376°W, 49.07848°N) in the Washington area, with the farthest distance being 1184.4 km and 985 km respectively.we set up a classification prediction model for better classification. According to the image upload time and location, SVM and neural network are used for classification prediction, and the classification accuracy is 74.26% and 97.60%, respectively, and the neural network has higher classification accuracy. So we choose the neural network. 

A hybrid model for student grade prediction using support vector machine and neural network

2020 ◽  
pp. 1-11
Author(s):  
Jianjun Miao
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Machine Learning Algorithms ◽  
Experimental Comparison ◽  
Support Vector ◽  
Advantages And Disadvantages ◽  
Grade Prediction ◽  
The Neural Network ◽  
Neural Network Algorithm ◽  
Classification Prediction

It is difficult for the intelligent teaching system in colleges to effectively predict student grade, which makes it difficult to formulate follow-up teaching strategies. In order to improve the effect of student grade prediction, this study improves the neural network algorithm, combines support vector machines to build a student grade prediction model, and uses PCA to reduce the dimensionality of the sample data. The specific operation is realized by SPSS software. Moreover, this study removes redundant information inside the input vector and compresses multiple features into a few typical features as much as possible. In addition, the research set a control experiment to analyze the performance of the research model and compare the advantages and disadvantages of the classification prediction effect of traditional machine learning algorithms and neural network algorithms. Through experimental comparison, we can see that the model constructed in this paper has certain advantages in all aspects of parameter performance, and the prediction model proposed in this study has certain effects.

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%.

Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks

2019 ◽  
Vol 14 (2) ◽  
pp. 158-164 ◽  
Author(s):  
G. Emayavaramban ◽  
A. Amudha ◽  
T. Rajendran ◽  
M. Sivaramkumar ◽  
K. Balachandar ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Network Models ◽  
Prosthetic Hand ◽  
Neural Network Models ◽  
The Neural Network ◽  
Semg Signals

Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.

Study on the Deformation Forecasting for the Tunnel Surrounding Rock Using the Neural Network

2013 ◽  
Vol 353-356 ◽  
pp. 828-832
Author(s):  
Guo Feng Wang ◽  
Wen Zhao ◽  
Yong Ping Guan ◽  
Lei Liang
Keyword(s):  
Neural Network ◽  
Maximum Error ◽  
Mining Area ◽  
Surrounding Rock ◽  
Rock Deformation ◽  
The Neural Network ◽  
Sublevel Caving ◽  
Iron Mine ◽  
Set Up ◽  
Surrounding Rock Deformation

The non-pillar sublevel caving method is used in Iron Mine in Banshi. In the mining area, there are many folds and faults, the inclination of ore body changes greatly, and ore and rock are fragmentized. The tunnel often collapsed and the surrounding rock deformation was getting large during the construction stage. Using the data of tunnel surrounding rock deformation, we adopt the neural network method to set up the mapping relation between the tunnel surrounding rock deformation and the project factors, including tunnel deepness, tunnel dimension, measuring time and surrounding rock quality. The analyzing results show that the maximum error between the forecast and the testing data is 13%, which indicates that this method is useful and feasible to the mining engineering. Key words: rock pressure; measure, deformation of the tunnel surrounding rock; neural network; data normalization; mapping

Use BP Neural Network Set up the Corrosion Prediction Model of Low Temperature Parts of Atmospheric Pressure Device

2012 ◽  
Vol 152-154 ◽  
pp. 1138-1142 ◽  
Author(s):  
Yu Guang Fan ◽  
Zai Dong Piao ◽  
Bing Chen ◽  
Hong Xian Lin ◽  
Yang Yang
Keyword(s):  
Neural Network ◽  
Low Temperature ◽  
Prediction Model ◽  
Corrosion Protection ◽  
Bp Neural Network ◽  
Atmospheric Pressure ◽  
Ph Value ◽  
Set Up

In research of the low temperature parts of atmospheric pressure device, by using BP neural network, the connection of PH value, Cl-, H2S and Fe+2 was setup which can predict Fe+2 content accurately, and obtain the requirement accuracy, hence more accurate corrosion can be predicted and providing more suggests for corrosion protection.

scholarly journals An Automatic Logistics System using Artificial Intelligence

2019 ◽  
Vol 8 (2S4) ◽  
pp. 924-926
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Optimal Route ◽  
Logistics System ◽  
The Neural Network ◽  
Artificial Intelligence Technology ◽  
Artificial Neural ◽  
Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

scholarly journals Deep learning application for box-office evaluation of images

2020 ◽  
Vol 44 (1) ◽  
pp. 127-132
Author(s):  
V.G. Efremtsev ◽  
N.G. Efremtsev ◽  
E.P. Teterin ◽  
P.E. Teterin ◽  
V.V. Gantsovsky
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sample Preparation ◽  
Convolutional Neural Network ◽  
Preliminary Data ◽  
Classification Accuracy ◽  
Training Sample ◽  
Data Preparation ◽  
Box Office ◽  
The Neural Network

The possibility of application a convolutional neural network to assess the box-office effect of digital images is reviewed. We studied various conditions for sample preparation, optimizer algorithms, the number of pixels in the samples, the size of the training sample, color schemes, compression quality, and other photometric parameters in view of effect on training the neural network. Due to the proposed preliminary data preparation, the optimum of the architecture and hyperparameters of the neural network we achieved a classification accuracy of at least 98%.

scholarly journals The Advanced Algorithmic Method for Navigation System Correction of Spacecraft

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Danhe Chen ◽  
K. A. Neusypin ◽  
Xiang Zhang ◽  
Chuangge Wang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Navigation System ◽  
Parallel Implementation ◽  
The Self ◽  
Self Organization ◽  
Error Prediction ◽  
Measurement Systems ◽  
The Neural Network ◽  
Autonomous Mode

In this paper an advanced method for the navigation system correction of a spacecraft using an error prediction model of the system is proposed. Measuring complexes have been applied to determine the parameters of a spacecraft and the processing of signals from multiple measurement systems is carried out. Under the condition of interference in flight, when the signals of external system (such as GPS) disappear, the correction of navigation system in autonomous mode is considered to be performed using an error prediction model. A modified Volterra neural network based on the self-organization algorithm is proposed in order to build the prediction model, and the modification of algorithm indicates speeding up the neural network. Also, three approaches for accelerating the neural network have been developed; two examples of the sequential and parallel implementation speed of the system are presented by using the improved algorithm. In addition, simulation for a returning spacecraft to atmosphere is performed to verify the effectiveness of the proposed algorithm for correction of navigation system.

Study of the Concrete Block Temperature Intelligent Prediction Model during the Construction

2013 ◽  
Vol 663 ◽  
pp. 68-71
Author(s):  
Kai Jiang ◽  
Yi Hong Zhou ◽  
Yao Ying Huang ◽  
Shao Wu Zhou ◽  
Dan Dan Liu
Keyword(s):  
Neural Network ◽  
Statistical Model ◽  
Prediction Model ◽  
Temperature Variation ◽  
Network Model ◽  
Neural Network Model ◽  
Concrete Block ◽  
The Neural Network ◽  
Concrete Temperature ◽  
Block Temperature

The explicit statistical model of concrete temperature variation is difficult to reasonably reflect the nonlinear relationship between the historical information and future information. This article is based on neural network intelligence tools and uses the neural network model to describe the concrete temperature variation during the construction. The relationships between the concrete temperature and initial temperature (pouring temperature), environmental temperature, the cement hydration heat temperature increase, water cooling effect and other factors are nonlinear. Establishing the neural network model of concrete temperature variation, exploring the historical temperature information could predict the future temperature information. Applying the intelligent prediction model to a construction project shows that when compared with the traditional explicit temperature statistical model, the temperature neural network prediction model established in this paper has obvious simplicity and superiority.

GA-BP Neural Network Based Tire Noise Prediction

2012 ◽  
Vol 443-444 ◽  
pp. 65-70 ◽  
Author(s):  
Yong Che ◽  
Wang Xin Xiao ◽  
Li Jun Chen ◽  
Zhi Chu Huang
Keyword(s):  
Neural Network ◽  
Optimal Solution ◽  
Noise Prediction ◽  
Bp Network ◽  
Tire Noise ◽  
The Neural Network ◽  
Highly Nonlinear ◽  
Neural Network Structure ◽  
Trained Neural Network ◽  
Set Up

According to the complexity and the highly nonlinear characteristics of the tire sound, various parameters affecting tire noise were analyzed. By employing neural network a new method of tire noise prediction was proposed. Combining BP neural networks with genetic algorithms the noise prediction model was set up. In order to effectively predict tire noise, the neural network structure was designed and the input and output parameters of the network were determined. The genetic algorithm was added to the BP network in order to optimize initial weights and search out the optimal solution of the network. Applying laboratory drum method large amounts of tire noise test samples were obtained to train the BP network. Trained neural network can accurately predict tire noise in range of typical frequency bands. The results show that precision of this method is sufficient and the prediction effect is better.

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