The number of processing elements in hidden-layer of back-propagation neural network for karyotyping ~genetic diagnosis\

2002 ◽  
Author(s):  
Jongman Cho ◽  
Seunghong Hong
Keyword(s):  
Neural Network ◽  
Genetic Diagnosis ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Processing Elements ◽  
Hidden Layer

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 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.

The Effects of ZigBee Position with Different Hidden Layers in Back-Propagation Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 1761-1767
Author(s):  
Fang Tsung Liu ◽  
Ceweng Erh Weng ◽  
Chien Ming Huang ◽  
Chang Yan Yang ◽  
Huang Chu Huang
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Back Propagation ◽  
Signal Strength ◽  
Back Propagation Neural Network ◽  
Wireless Sensor ◽  
Measured Signal ◽  
The Neural Network ◽  
Hidden Layer

In this paper, the research topic is that the expert experience is established by the size of the measured signal strength of wireless sensor networks and put the strength of the actual collection of historical data into the neural network model. In order to get the minimize error we use the errors to modify the weights and threshold of the neural network links. We compare the differences of hidden layer neural network and the experimental results. We set up a wireless sensor networks environment to collect the measurement values of signal strength (RSSI) and develop an indoor positioning system.

Research on Automated Forex Trading System Based on BP Neural Network

2013 ◽  
Vol 753-755 ◽  
pp. 3080-3083
Author(s):  
Li Meng ◽  
Yang Sun
Keyword(s):  
Neural Network ◽  
Exchange Rate ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Foreign Exchange Rate ◽  
Feedforward Network ◽  
Trading System ◽  
Hidden Layer ◽  
Exchange Rate Prediction

This paper reports on an expert advisor for forex trading based on Back Propagation Neural Network (BPNN) in MetaTrader4 platform. A single hidden layer feedforward network was established for foreign exchange rate prediction. Trading rules based on the prediction results was designed and realized. Finally, we optimized the parameters according to the profitability performed on EUR/USD, GBP/USD currency pairs separately. The optimized results are able to achieve good results in the training series. In the test series, the strategies are consistently profitable for at least the first twenty days. It is concluded that the BPNN based model do have the ability to make profits from the experimental currency pairs for the period investigated.

scholarly journals Optimization of Copper Electroforming Process Parameters Based on Double Hidden Layer BP Neural Network

Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1157
Author(s):  
Feng Ji ◽  
Chao Chen ◽  
Yongfei Zhao ◽  
Byungwon Min
Keyword(s):  
Neural Network ◽  
Tensile Strength ◽  
Back Propagation ◽  
Pulse Frequency ◽  
Back Propagation Neural Network ◽  
Copper Layer ◽  
Process Conditions ◽  
Optimal Parameters ◽  
Hidden Layer ◽  
Experimental Values

In order to optimize the pulse electroforming copper process, a double hidden layer BP (back propagation) neural network was constructed. Through sample training, the mapping relationship between electroforming copper process conditions and target properties was accurately established, and the prediction of microhardness and tensile strength of the electroforming layer in the pulse electroforming copper process was realized. The predicted results were verified by electrodeposition copper test in copper pyrophosphate solution system with pulse power supply. The results show that the microhardness and tensile strength of copper layer predicted by “3-4-3-2” structure double hidden layer neural network are very close to the experimental values, and the relative error is less than 2.82%. In the parameter range, the microhardness of copper layer is between 100.3~205.6 MPa and the tensile strength is between 165~485 MPa. When the microhardness and tensile strength are optimal, the corresponding range of optimal parameters are as follows: current density is 2–3 A·dm−2, pulse frequency is 1.5–2 kHz and pulse duty cycle is 10–20%.

Modelling of the Parameters of EDM in Gas Based on Back Propagation Neural Network

2018 ◽  
Vol 926 ◽  
pp. 11-16
Author(s):  
Yan Cherng Lin ◽  
Han Ming Chow ◽  
Hsin Min Lee ◽  
Jia Feng Liu
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Machining Parameters ◽  
Ann Model ◽  
Output Layer ◽  
Input Layer ◽  
Hidden Layer ◽  
Machining Characteristics

The aim of this study is to develop a predicted model of the machining parameters with relation to material removal rate (MRR) and surface roughness (SR) of electrical discharge machining (EDM) in gas. The experimental tasks were implemented by a specific design of experimental method named central composite design (CCD) method. The mathematical prediction models between operating parameters and machining characteristics based on artificial neural network (ANN) were established. The back propagation neural network (BPNN) was employed to construct the architecture of the input layer, the hidden layer and the output layer to build the ANN model. Moreover, the weight and the bias values were examined by the steepest descent method (SDM) with the training data. Thus, the suitable ANN models were established with the acquired weight and bias values. The essential parameters of the EDM in gas such as peak current (Ip), pulse duration (tp), gas pressure (GP), servo reference voltage (Sv) were chosen to investigate the effects on MRR and SR. The developed ANN model with 4 input variables on the input layer, one hidden layer with 5 neurons, and 2 response variables on the output layer was obtained by the training with 30 experimental data. Moreover, as the prediction values obtained from the ANN compared with the 5 testing data, the error falls in the rage of 5% indicating the developed ANN is appropriate and predictable. Moreover, the developed ANN model can be used to predict the machining characteristics such as MRR and SR for the EDM in gas with various parameter settings.

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