scholarly journals Determining Neuronal Number in Each Hidden Layer Using Earthquake Catalogues as Training Data in Training an Embedded Back Propagation Neural Network for Predicting Earthquake Magnitude

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 52582-52597 ◽  
Author(s):  
Jyh-Woei Lin ◽  
Chun-Tang Chao ◽  
Juing-Shian Chiou
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Earthquake Magnitude ◽  
Training Data ◽  
Earthquake Catalogues ◽  
Neuronal Number ◽  
Hidden Layer

THE STAG OIL FIELD FORMATION EVALUATION: A NEURAL NETWORK APPROACH

1999 ◽  
Vol 39 (1) ◽  
pp. 451 ◽  
Author(s):  
H. Crocker ◽  
C.C. Fung ◽  
K.W. Wong
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Oil Field ◽  
Training Data ◽  
Ann Model ◽  
Neural Network Approach ◽  
Data Set ◽  
Formation Evaluation ◽  
Core Data

The producing M. australis Sandstone of the Stag Oil Field is a bioturbated glauconitic sandstone that is difficult to evaluate using conventional methods. Well log and core data are available for the Stag Field and for the nearby Centaur–1 well. Eight wells have log data; six also have core data.In the past few years artificial intelligence has been applied to formation evaluation. In particular, artificial neural networks (ANN) used to match log and core data have been studied. The ANN approach has been used to analyse the producing Stag Field sands. In this paper, new ways of applying the ANN are reported. Results from simple ANN approach are unsatisfactory. An integrated ANN approach comprising the unsupervised Self-Organising Map (SOM) and the Supervised Back Propagation Neural Network (BPNN) appears to give a more reasonable analysis.In this case study the mineralogical and petrophysical characteristics of a cored well are predicted from the 'training' data set of the other cored wells in the field. The prediction from the ANN model is then used for comparison with the known core data. In this manner, the accuracy of the prediction is determined and a prediction qualifier computed.This new approach to formation evaluation should provide a match between log and core data that may be used to predict the characteristics of a similar uncored interval. Although the results for the Stag Field are satisfactory, further study applying the method to other fields is required.

Prediction of Yarn Quality Based on BP Neural Network

2011 ◽  
Vol 331 ◽  
pp. 449-453
Author(s):  
Jing Yuan ◽  
Ying Lin Li ◽  
Su Ying Chen
Keyword(s):  
Neural Network ◽  
Mathematical Models ◽  
Network Architecture ◽  
Raw Materials ◽  
Nonlinear Approximation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Yarn Quality ◽  
Spinning Process ◽  
Hidden Layer

As the quality of yarn and the fiber indicators are nonlinear relationship, the traditional mathematical models or empirical formula has been unable to accurately resolve the problem. In view of artificial neural networks do not need to build accurate mathematical models, applicable to solving the problem of yarn quality prediction. In this paper, good nonlinear approximation ability of BP (Back Propagation) neural network be used, the use of neural network toolbox of MATLAB functions for modeling, good results was obtained. Prediction model set a hidden layer, using three-tier network architecture, and take the input layer 4 nodes, hidden layer 8 nodes and output layer 2 nodes. According to forecast results, can ensure the yarn quality effectively, use of raw materials rationally, to achieve optimal distribution of cotton. Meanwhile, the spinning process design can also be provided validation, for the development of new products to provide a theoretical basis.

Forecasting Stock Price Based on Back Propagation Neural Network

2014 ◽  
Vol 1006-1007 ◽  
pp. 1031-1034
Author(s):  
Li Zhang ◽  
Qing Yang Xu ◽  
Chao Chen ◽  
Zeng Jun Bao
Keyword(s):  
Neural Network ◽  
Stock Price ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Method ◽  
Price Forecast ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Hidden Layer ◽  
And Training

The stock market is a nonlinear dynamics system with enormous information, which is difficult to predict effectively by traditional methods. The model of stock price forecast based on BP Neutral-Network is put forward in this article. The paper try to find the way how to predictive the stock price. Exhaustive method is used for the hidden layer neurons and training method determination. Finally the experiment results show that the algorithm get better performance in stock price prediction.

scholarly journals Peramalan Jumlah Penumpang Kereta Api di Indonesia dengan Resilient Back-Propagation (Rprop) Neural Network

2018 ◽  
Vol 4 (2) ◽  
pp. 90-99
Author(s):  
Mertha Endah Ervina ◽  
Rini Silvi ◽  
Intaniah Ratna Nur Wisisono
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Prediction Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Percentage Error ◽  
Network Architectures ◽  
Forecasting Accuracy ◽  
Resilient Back Propagation ◽  
Very High

Train scheduling affects the level of customer satisfaction and profitability of the train service provider. The prediction method of Back-propagation Neural Network (BPNN) has relatively slow convergence. Therefore, this study uses Resilient Back-propagation (Rprop) because it has a more fast convergence and high accuracy. The model produced is a model for Jabodetabek, Java (non-Jabodetabek), Sumatra, and Indonesia. From the results of data analysis conducted, it can be concluded that the performance of neural network model with Resilient Back-propagation (Rprop) formed from training data gives very accurate prediction accuracy level with mean absolute percentage error (MAPE) less than 10% for each model. Then forecasting for the next 12 months conducted and the results compared with the data testing, Rprop provides a very high forecasting accuracy with MAPE value below 10%. The MAPE value for each forecasting the number of rail passengers is 7.50% for Jabodetabek, 5.89% for Java (non-Jabodetabek), 5.36% for Sumatra and 4.80% for Indonesia. That is, four neural network architectures with Rprop can be used for this case with very accurate forecasting results.

scholarly journals Importance Degree Evaluation of Spare Parts Based on Clustering Algorithm and Back-Propagation Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Shoujing Zhang ◽  
Xiaofan Qin ◽  
Sheng Hu ◽  
Qing Zhang ◽  
Bochao Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Inventory Management ◽  
Clustering Algorithm ◽  
Back Propagation ◽  
Spare Part ◽  
Back Propagation Neural Network ◽  
Spare Parts ◽  
Training Data ◽  
Importance Value ◽  
The Impact

The quantitative evaluation of the importance degree of spare parts is essential as spare parts’ maintenance is critical for inventory management. Most of the methods used in previous research are subjective. For this reason, an accurate method for the evaluation of the importance degree combining an improved clustering algorithm with a back-propagation neural network (BPNN) is proposed in the present paper. First, we classified the spare parts by analyzing their historical maintenance and inventory data. Second, we evaluated the effectiveness of classification using the Davies–Bouldin index and the Calinski–Harabasz indicator and verified it using the training data. Finally, we used BPNN to determine the training data necessary for an accurate assessment of the importance degree of spare parts. The previous importance evaluation methods were susceptible to subjective factors during the evaluation process. The model established in this paper used the actual data of the company for machine learning and used the improved clustering algorithm to implement training and classification of spare parts data. The importance value of each spare part was output, which additionally reduced the impact of subjective factors on the importance evaluation. At the same time, the use of less data to evaluate the importance of spare parts was achieved, which improved the evaluation efficiency.

CLASSIFICATION OF sEMG SIGNALS USING INTEGRATED NEURAL NETWORK WITH SMALL SIZED TRAINING DATA

2012 ◽  
Vol 24 (04) ◽  
pp. 365-376 ◽  
Author(s):  
Baofeng Sun ◽  
Wanzhong Chen
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Accurate Method ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Semg Signal ◽  
Elman Neural Network ◽  
Robotic Devices ◽  
Semg Signals

The sEMG (Surface electromyography) signals detected from activated muscles can be used as a control source for prosthesis. So an efficient and accurate method for the classification of sEMG signal patterns has become a hot research in recent years. Artificial neural network is a popular used method in this field, however, most neural networks require large numbers of samples in the training stage to obtain the potential relationships between input feature vectors and the outputs. In this paper, Integrated back propagation neural network (IBPNN) is used to classify sEMG signals acquired during five different hand motions. The correct classification rates of IBPNN for the five hand movements are significantly higher than that of BPNN and Elman neural network. This reveals that IBPNN achieves the best performance with a small sized training data and can be used in control systems on prosthetic hands and other robotic devices based on electromyography pattern recognition.

System Identification of UAV Alap-Alap Using Back Propagation Neural Network

2013 ◽  
Vol 373-375 ◽  
pp. 1212-1219
Author(s):  
Afrias Sarotama ◽  
Benyamin Kusumoputro
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Training Data ◽  
Data Sets ◽  
Input Output ◽  
Data Set ◽  
Flight Data ◽  
The Neural Network ◽  
Aerial Vehicle

A good model is necessary in order to design a controller of a system off-line. It is especially beneficial in the implementation of new advanced control schemes in Unmanned Aerial Vehicle (UAV). Considering the safety and benefit of an off-line tuning of the UAV controllers, this paper identifies a dynamic MIMO UAV nonlinear system which is derived based on the collection of input-output data taken from a test flights (36250 samples data). These input-output sample flight data are grouped into two flight data sets. The first flight data set, a chirp signal, is used for training the neural network in order to determine parameters (weights) for the network. Validation of the network is performed using the second data set, which is not used for training, and is a representation of UAV circular flight movement. An artificial neural network is trained using the training data set and thereafter the network is excited by the second set input data set. The predicted outputs based on our proposed Neural Network model is similar to the desired outputs (roll, pitch and yaw) which has been produced by real UAV system.

scholarly journals Simulation design of an Intelligent system for Automotive transmission Gearbox Based on FPGA

2018 ◽  
Vol 6 (2) ◽  
pp. 395-411
Author(s):  
Azzad Bader SAEED
Keyword(s):  
Neural Network ◽  
Intelligent System ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Speed Ratio ◽  
Simulation Design ◽  
Automotive Transmission ◽  
Field Programmable ◽  
Hidden Layer

In this paper, an artificial  intelligent system has been designed, realized, and downloaded into  FPGA (Field Programmable Gate Array), which is used to control five speed ratio steps ( 1,2,3,4,5) of an electrically controlled type of  automotive transmission gearbox of a vehicle, the first speed ratio step (1) is characterized by the  highest torque, a lowest velocity, while, the  fifth step is characterized by the lowest torque, and highest velocity.The Back-propagation neural network has been used as the intelligent system for the proposed system. The proposed neural network is composed from   eight neurons in the input layer, five neurons in the hidden layer, and five neurons in the output layer. For real downloading into the FPGA, Satlins and Satlin linear activation function has been used for the hidden and output layers respectively. The training function Trainlm ( Levenberg-Marqurdt training) has been used as a learning method for the proposed neural network, which it has a powerful algorithm. The proposed simulation system has been designed and downloaded into the FPGA using MATLAB and ISE Design Suit software packages.

scholarly journals Perancangan Model Jaringan Syaraf Tiruan Propagasi Balik Untuk Mengestimasi Curah Hujan

2012 ◽  
Vol 9 (2) ◽  
Author(s):  
Elohansen Padang
Keyword(s):  
Neural Network ◽  
Gradient Descent ◽  
Back Propagation ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Rainfall Data ◽  
Training Method ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer

This research was conducted to investigate the ability of backpropagation artificial neural network in estimating rainfall. Neural network used consists of input layer, 2 hidden layers and output layer. Input layer consists of 12 neurons that represent each input; first hidden layer consists of 12 neurons with activation function tansig, while the second hidden layer consists of 24 neurons with activation function logsig. Output layer consists of 1 neuron with activation function purelin. Training method used is the method of gradient descent with momentum. Training method used is the method of gradient descent with momentum. Learning rate and momentum parameters defined respectively by 0.1 and 0.5. To evaluate the performance of the network model to recognize patterns of rainfall data is used in Biak city rainfall data from January 1997 - December 2008 (12 years). This data is divided into 2 parts, namely training and testing data using rainfall data from January 1997-December 2005 and data estimation using rainfall data from January 2006-December 2008. From the results of this study concluded that rainfall patterns Biak town can be recognized quite well by the model of back propagation neural network. The test results and estimates of the model results testing the value of R = 0.8119, R estimate = 0.53801, MAPE test = 0.1629, and MAPE estimate = 0.6813.

Optimization of the Composite Truncated Cone Structure Layers under Buckling Load

2012 ◽  
Vol 225 ◽  
pp. 144-149
Author(s):  
Hadi Samareh Salavati Pour ◽  
Mojtaba Sadighi ◽  
Abdolvahed Kami
Keyword(s):  
Neural Network ◽  
Finite Element ◽  
Back Propagation ◽  
Activation Function ◽  
Buckling Load ◽  
Training Data ◽  
Training Algorithm ◽  
Cone Structure ◽  
Truncated Cone ◽  
Hidden Layer

The orientation of fibers in the layers is an important factor that must be obtained in order to predict how well the finished composite product will perform under real-world working conditions. In this research, a five-layer glass-epoxy composite truncated cone structure under buckling load was considered. The simulation of the structure was done utilizing finite element method and was confirmed comparing with the published experimental results. Then the effect of different orientation of fibers on the buckling load was considered. For this, a computer programing was developed to compute the buckling load for different orientations of fibers in each layer. These orientations were produced randomly with the delicacy of 15 degrees. Finally, neural network and genetic algorithm methods were utilized to obtain the optimum orientations of fibers in each layer using the training data obtained from finite element simulation. There are many parameters such as the number of hidden layers, the number of neurons in each hidden layer, the training algorithm, the activation function and so on which must be specified properly in development of a neural network model. The number of hidden layers and number of neurons in each layer was obtained by try and error method. In this study, multilayer back-propagation (BP) neural network with the Levenberg-Marquardt training algorithm (trainlm) was used. Finally, the results showed that the truncated cone with optimum layers withstand considerably more buckling load.

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