scholarly journals Back Propagation Neural Network(BPNN) and Sigmoid Activation Function in Multi-Layer Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 216
Author(s):  
Renas Rajab Asaad ◽  
Rasan I. Ali
Keyword(s):  
Neural Network ◽  
Logic Gate ◽  
Back Propagation ◽  
Main Idea ◽  
Activation Function ◽  
Back Propagation Neural Network ◽  
Linear Threshold ◽  
Non Linear ◽  
Hidden Layer ◽  
Sigmoid Activation Function

Back propagation neural network are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural network for back propagation of errors, and sigmoid activation function. This neural network to map non-linear threshold gate. The non-linear used to classify binary inputs (x1, x2) and passing it through hidden layer for computing coefficient_errors and gradient_errors (Cerrors, Gerrors), after computing errors by (ei = Output_desired- Output_actual) the weights and thetas (ΔWji = (α)(Xj)(gi), Δϴj = (α)(-1)(gi)) are changing according to errors. Sigmoid activation function is = sig(x)=1/(1+e-x) and Derivation of sigmoid is = dsig(x) = sig(x)(1-sig(x)). The sig(x) and Dsig(x) is between 1 to 0.

Neural networks and Sigmoid Activation Function in Multi-Layer Networks

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Renas M Redwan
Keyword(s):  
Neural Networks ◽  
Logic Gate ◽  
Back Propagation ◽  
Main Idea ◽  
Activation Function ◽  
Back Propagation Neural Networks ◽  
Linear Threshold ◽  
Non Linear ◽  
Hidden Layer ◽  
Sigmoid Activation Function

Back propagation neural networks are known for computing the problems that cannot easily be computed (huge datasets analysis or training) in artificial neural networks. The main idea of this paper is to implement XOR logic gate by ANNs using back propagation neural networks for back propagation of errors, and sigmoid activation function. This neural networks to map non-linear threshold gate. The non-linear used to classify binary inputs ( ) and passing it through hidden layer for computing  and  ( ), after computing errors by ( ) the weights and thetas ( ) are changing according to errors. Sigmoid activation function is =  and Derivation of sigmoid is = . The sig(x) and Dsig(x) is between 1 to 0.

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.

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.

NEW METHOD OF NEURON DESIGN BASED ON DISCRETE Z-FUNCTION TO ADAPT THE CHANGE OF INTEGRATED VEHICLE STABILITY CONTROL ORDER

2009 ◽  
Vol 08 (03) ◽  
pp. 253-285 ◽  
Author(s):  
M. HARLY ◽  
I. N. SUTANTRA ◽  
H. P. MAURIDHI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Back Propagation ◽  
Activation Function ◽  
Zero Order ◽  
Vehicle Stability ◽  
Adaptive Control System ◽  
Control Performance ◽  
Fixed Order ◽  
Hidden Layer

Fixed order neural networks (FONN), such as high order neural network (HONN), in which its architecture is developed from zero order of activation function and joint weight, regulates only the number of weight and their value. As a result, this network only produces a fixed order model or control level. These obstacles, which affect preceeding architectures, have been performing finite ability to adapt uncertainty character of real world plant, such as driving dynamics and its desired control performance. This paper introduces a new concept of neural network neuron. In this matter, exploiting discrete z-function builds new neuron activation. Instead of zero order joint weight matrices, the discrete z-function weight matrix will be provided to realize uncertainty or undetermined real word plant and desired adaptive control system that their order has probably been changing. Instead of using bias, an initial condition value is developed. Neural networks using new neurons is called Varied Order Neural Network (VONN). For optimization process, updating order, coefficient and initial value of node activation function uses GA; while updating joint weight, it applies both back propagation (combined LSE-gauss Newton) and NPSO. To estimate the number of hidden layer, constructive back propagation (CBP) was also applied. Thorough simulation was conducted to compare the control performance between FONN and MONN. In order to control, vehicle stability was equipped by electronics stability program (ESP), electronics four wheel steering (4-EWS), and active suspension (AS). 2000, 4000, 6000, 8000 data that are from TODS, a hidden layer, 3 input nodes, 3 output nodes were provided to train and test the network of both the uncertainty model and its adaptive control system. The result of simulation, therefore, shows that stability parameter such as yaw rate error, vehicle side slip error, and rolling angle error produces better performance control in the form of smaller performance index using FDNN than those using MONN.

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.

Prediction of Surface Quality and Parameter in Bearing Convex Raceway Finishing

2007 ◽  
Vol 24-25 ◽  
pp. 361-370
Author(s):  
Bin Tao ◽  
Xu Yue Wang ◽  
H.Z. Zhen ◽  
Wen Ji Xu
Keyword(s):  
Neural Network ◽  
Surface Quality ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Processing Parameters ◽  
Machining Parameters ◽  
Belt Grinding ◽  
Linear Behavior ◽  
Non Linear ◽  
Hidden Layer

Electrochemical abrasive belt grinding (ECABG) technology, which has the advantage over conventional stone super-finishing, has been applied in bearing raceway super-finishing. However, the finishing effect of ECABG is dominated by many factors, which relationship is so complicated that appears non-linear behavior. Therefore, it is difficult to predict the finishing results and select the processing parameters in ECABG. In this paper, Back-Propagation (BP) neural network is proposed to solve this problem. The non-linear relationship of machining parameters was established based on the experimental data by applying one-hidden layer BP neural networks. The comparison between the calculated results of the BP neural network and experimental results under the corresponding conditions was carried out, and the results indicates that it is feasible to apply BP neural network in determining the processing parameters and forecasting the surface quality effects in ECABG.

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.

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.

Sign in / Sign up

Export Citation Format

Share Document