scholarly journals Theoretical Investigations for the Verification of Shear Centre and Deflection of Sigma Section by Back Propagation Neural Network Using Python

2019 ◽  
Vol 65 (2) ◽  
pp. 181-192
Author(s):  
S. Janani ◽  
R. Thenmozhi ◽  
L.S. Jayagopal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Error Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Average Error ◽  
Average Error Rate ◽  
Proposed Model ◽  
Theoretical Investigations ◽  
Shear Centre

AbstractThe most important challenges in the construction field is to do the experimentation of the designing at real time. It leads to the wastage of the materials and time consuming process. In this paper, an artificial neural network based model for the verification of sigma section characteristics like shear centre and deflection are designed and verified. The physical properties like weight, depth, flange, lip, outer web, thickness, and area to bring shear centre are used in the model. Similarly, weight, purlin centres with allowable loading of different values used in the model for deflection verification. The overall average error rate as 1.278 percent to the shear centre and 2.967 percent to the deflection are achieved by the model successfully. The proposed model will act as supportive tool to the steel roof constructors, engineers, and designers who are involved in construction as well as in the section fabricators industry.

The Application of Probabilistic Neural Network in Speech Recognition Based on Partition Clustering

2012 ◽  
Vol 263-266 ◽  
pp. 2173-2178
Author(s):  
Xin Guang Li ◽  
Min Feng Yao ◽  
Li Rui Jian ◽  
Zhen Jiang Li
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Clustering Algorithm ◽  
Probabilistic Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Data Sets ◽  
Data Set ◽  
Proposed Model ◽  
Partition Clustering

A probabilistic neural network (PNN) speech recognition model based on the partition clustering algorithm is proposed in this paper. The most important advantage of PNN is that training is easy and instantaneous. Therefore, PNN is capable of dealing with real time speech recognition. Besides, in order to increase the performance of PNN, the selection of data set is one of the most important issues. In this paper, using the partition clustering algorithm to select data is proposed. The proposed model is tested on two data sets from the field of spoken Arabic numbers, with promising results. The performance of the proposed model is compared to single back propagation neural network and integrated back propagation neural network. The final comparison result shows that the proposed model performs better than the other two neural networks, and has an accuracy rate of 92.41%.

Application of Neural Network in Urban Land Use Suitability Evaluation

2011 ◽  
Vol 474-476 ◽  
pp. 681-686
Author(s):  
Xiao Rui Zhang ◽  
Gang Chen
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Urban Land ◽  
Urban Land Use ◽  
Suitability Evaluation ◽  
Land Use Suitability ◽  
Artificial Neural

Urban land use suitability evaluation is the basic work of urban land use planning and management. The evaluation method is a core in urban land use suitability evaluation. Traditional urban land use suitability evaluation methods are GIS-based methods which often can not get satisfactory results for the complex nonlinear urban land use system. Artificial neural network is a frontier theory of complex non-linearity scientific and artificial intelligence science. It is a new method to evaluate urban land use suitability. This paper took the land use suitability evaluation of Hefei city as an example, building a back propagation neural network with 8 neurous of input layer, 5 neurons of hide layer and 3 neurons of output layer. The analysis shows: the high suitability area is 682.27 km2in Hefei city, being about 8.73% of the total study area; the middle suitability area is 5965.76 km2, or about 76.33% of the total area and the low suitability area is 1167.35 km2, or about 14.94% of the total area. The results reflect the actual situation in Hefei city. The study shows that the back propagation neural network model can overcome the shortcomings of traditional evaluation methods. It means that artificial neural network is suitable for urban land use suitability evaluation. This reflects that artificial neural network has great academic value and application prospect in urban land use suitability evaluation. It also reflects that this study can provide a new idea and method for urban land use suitability evaluation.

Modeling and Characterization of a Photovoltaic Array Based on Actual Performance Using Cascade-Forward Back Propagation Artificial Neural Network

2015 ◽  
Vol 137 (4) ◽  
Author(s):  
Ammar Mohammed Ameen ◽  
Jagadeesh Pasupuleti ◽  
Tamer Khatib ◽  
Wilfried Elmenreich ◽  
Hussein A. Kazem
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Measured Data ◽  
Mean Bias Error ◽  
Output Current ◽  
Pv System ◽  
Proposed Model ◽  
Artificial Neural ◽  
Predicted Values

This paper proposes a novel prediction model for photovoltaic (PV) system output current. The proposed model is based on cascade-forward back propagation artificial neural network (CFNN) with two inputs and one output. The inputs are solar radiation and ambient temperature, while the output is output current. Two years of experimental data for a 1.4 kWp PV system are utilized in this research. The monitored performance is recorded every 2 s in order to consider the uncertainty of the system’s output current. A comparison between the proposed model and other empirical and statistical models is done in this paper as well. Moreover, the ability of the proposed model to predict performance with high uncertainty rate is validated. Three statistical values are used to evaluate the accuracy of the proposed model, namely, mean absolute percentage error (MAPE), mean bias error (MBE), and root mean square error (RMSE). These values are used to measure the deviation between the actual and the predicted data in order to judge the accuracy of the proposed model. A simple estimation of the deviation between the measured value and the predicted value with respect to the measured value is first given by MAPE. After that, the average deviation of the predicted values from measured data is estimated by MBE in order to indicate the amount of the overestimation/underestimation in the predicted values. Third, the ability of predicting future records is validated by RMSE, which represents the variation of the predicted data around the measured data. Eventually, the percentage of MBE and RMSE is calculated with respect to the average value of the output current so as to present better understating of model’s accuracy. The results show that the MAPE, MBE, and RMSE of the proposed model are 7.08%, −0.21 A (−4.98%), and 0.315 A (7.5%), respectively. In addition to that, the proposed model exceeds the other models in terms of prediction accuracy.

scholarly journals Vibration Reliability Analysis of Drum Brake Using the Artificial Neural Network and Important Sampling Method

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Zhou Yang ◽  
Unsong Pak ◽  
Cholu Kwon
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reliability Analysis ◽  
Sampling Method ◽  
Back Propagation ◽  
Simulation Method ◽  
Back Propagation Neural Network ◽  
Element Analysis ◽  
Analysis Methods ◽  
Artificial Neural

This research aims to evaluate the calculation accuracy and efficiency of the artificial neural network-based important sampling method (ANN-IS) on reliability of structures such as drum brakes. The finite element analysis (FEA) result is used to establish the ANN sample in ANN-based reliability analysis methods. Because the process of FEA is time-consuming, the ANN sample size has a very important influence on the calculation efficiency. Two types of ANNs used in this study are the radial basis function neural network (RBF) and back propagation neural network (BP). RBF-IS and BP-IS methods are used to conduct reliability analysis on training samples of three different sizes, and the results are compared with several reliability analysis methods based on ANNs. The results show that the probability of failure of the RBF-IS method is closer to that of the Monte-Carlo simulation method (MCS) than those of other methods (including BP-IS). In addition, the RBF-IS method has better calculation efficiency than the other methods considered in this study. This research demonstrates that the RBF-IS method is well suited to structure reliability problems.

scholarly journals R-DEHM: CSI-Based Robust Duration Estimation of Human Motion with WiFi

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1421 ◽  
Author(s):  
Jijun Zhao ◽  
Lishuang Liu ◽  
Zhongcheng Wei ◽  
Chunhua Zhang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Motion Detection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Research Area ◽  
Human Motion ◽  
Average Error ◽  
Area Of Interest ◽  
Human Motion Detection ◽  
Average Error Rate ◽  
Duration Estimation

As wireless sensing has developed, wireless behavior recognition has become a promising research area, in which human motion duration is one of the basic and significant parameters to measure human behavior. At present, however, there is no consideration of the duration estimation of human motion leveraging wireless signals. In this paper, we propose a novel system for robust duration estimation of human motion (R-DEHM) with WiFi in the area of interest. To achieve this, we first collect channel statement information (CSI) measurements on commodity WiFi devices and extract robust features from the CSI amplitude. Then, the back propagation neural network (BPNN) algorithm is introduced for detection by seeking a cutting line of the features for different states, i.e., moving human presence and absence. Instead of directly estimating the duration of human motion, we transform the complex and continuous duration estimation problem into a simple and discrete human motion detection by segmenting the CSI sequences. Furthermore, R-DEHM is implemented and evaluated in detail. The results of our experiments show that R-DEHM achieves the human motion detection and duration estimation with the average detection rate for human motion more than 94% and the average error rate for duration estimation less than 8%, respectively.

Prediction of infection and death ratio of CoVID-19 virus in Turkey by using artificial neural network (ANN)

Coronaviruses ◽  
2020 ◽  
Vol 01 ◽  
Author(s):  
Andaç Batur Çolak
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Death Rate ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
World Health ◽  
Feed Forward Back Propagation ◽  
The World ◽  
Artificial Neural ◽  
Health Organization

Background: For the first time in December 2019 as reported in the Whuan city of China COVID-19 deadly virus, spread rapidly around the world and the first cases were seen in Turkey on March 11, 2020. On the same day, a pandemic was declared by the World Health Organization due to the rapid spread of the disease throughout the world. Methods: In this study, a multilayered perception feed-forward back propagation neural network has been designed for predicting the spread and mortality rate of COVID-19 virus in Turkey. COVID-19 data from six different countries were used in the design of the artificial neural network, which has 15 neurons in its hidden layer. 70% of these optimized data were used for training, 20% for validation and 10% for testing. Results: The resulting simulation results, COVID-19 virus in Turkey between 20 and 37 days showed the fastest to rise. The number of cases for the 20th day was predicted to be 13.845 and the 51st day for the 37th day. Conclusion: As for the death rate, it was predicted that a rapid rise on the 20th day would start and a slowdown around the 43rd day and progress towards the zero case point. The death rate for the 20th day was predicted to be 170 and the 43rd day for the 1.960s.

Offline Signature Recognition using Back Propagation Neural Network

2016 ◽  
Vol 4 (3) ◽  
pp. 678
Author(s):  
Asyrofa Rahmi ◽  
Vivi Nur Wijayaningrum ◽  
Wayan Firdaus Mahmudy ◽  
Andi Maulidinnawati A. K. Parewe
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recognition Accuracy ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Learning Rate ◽  
Signature Recognition ◽  
Artificial Neural Network Ann ◽  
Number Of Iterations

The signature recognition is a difficult process as it requires several phases. A failure in a phase will significantly reduce the recognition accuracy. Artificial Neural Network (ANN) believed to be used to assist in the recognition or classification of the signature. In this study, the ANN algorithm used is Back Propagation. A mechanism to adaptively adjust the learning rate is developed to improve the system accuracy. The purpose of this study is to conduct the recognition of a number of signatures so that can be known whether the recognition which is done by using the Back Propagation is appropriate or not. The testing results performed by using learning rate of 0.64, the number of iterations is 100, and produces an accuracy value of 63%.

scholarly journals Recognition of Human Facial Expressions Using DCT-DWT and Artificial Neural Network

2021 ◽  
pp. 2090-2098
Author(s):  
Wasan. Maddah Alaluosi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Facial Expressions ◽  
Emotional State ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Discrete Wavelet ◽  
Three Stages ◽  
Artificial Neural ◽  
Facial Images

Facial expressions are a term that expresses a group of movements of the facial fore muscles that is related to one's own human emotions. Human–computer interaction (HCI) has been considered as one of the most attractive and fastest-growing fields. Adding emotional expression’s recognition to expect the users’ feelings and emotional state can drastically improves HCI. This paper aims to demonstrate the three most important facial expressions (happiness, sadness, and surprise). It contains three stages; first, the preprocessing stage was performed to enhance the facial images. Second, the feature extraction stage depended on Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) methods. Third, the recognition stage was applied using an artificial neural network, known as Back Propagation Neural Network (BPNN), on database images from Cohen-Kanade. The method was shown to be very efficient, where the total rate of recognition of the three facial expressions was 92.9%.

Predicting Flow Stress of Four Types of Microalloyed Forging Steel at High Temperature by Artificial Neural Network Model

2017 ◽  
Vol 729 ◽  
pp. 75-79
Author(s):  
Hu Sen Jiang ◽  
Jin Wang ◽  
Li Hua Li ◽  
Hai Tao Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
High Temperature ◽  
Flow Stress ◽  
Constitutive Model ◽  
Relative Error ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Artificial Neural ◽  
Input Variables

Artificial neural network (ANN) gets a lot of applications in predicting flow stress of steels at high temperature. However, few studies have been devoted to simultaneously predict flow stress of several steels by ANN. The purpose of this paper is to determine the effect of ANN on simultaneously predicting flow stress of several steels. Based on the results of previous compression experiments of four types of microalloyed forging steel, using the mass percentage of major chemical composition of the steels, such as as C, Mn, Si and V, and deformation temperature, strain rate and strain as input variables, a three-layers back propagation neural network was established as the constitutive model for them. Standard statistical methods were employed to quantitatively measure the accuracy of predicted results by the model. The calculated correlation coefficient and the average relative error absolute value between the predicted values by the model and experimental values were 0.9982 and 2.4181%, respectively. In addition, the relative error between the two kinds of values was calculated, and for more than 89% samples, the relative error was within ± 5%. The results show that the developed constitutive model can predict the flow stress of the four types of microalloyed forging steel accurately and simultaneously.

Dynamic Load Balancing Using Hybrid Approach

2019 ◽  
Vol 9 (3) ◽  
pp. 75-88
Author(s):  
Sunita Gond ◽  
Shailendra Singh
Keyword(s):  
Neural Network ◽  
Load Balancing ◽  
Hybrid Approach ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Error Back Propagation ◽  
Proposed Model ◽  
Multiple Process ◽  
Trained Neural Network ◽  
Sequence Quality

Load balancing in a cloud environment for handling multiple process of different size is an important issue. Many advanced technologies are incorporated in the processes-based resource allocation which enhances the system efficiency. The steps of allotting resources to process can be done by taking data which helps to analyze and make important decisions at runtime. This article focuses on the allocation of cloud resources where two models were developed, the first was TLBO (Teacher Learning Based Optimization), a genetic algorithm which finds the correct position for the process to execute. Here, some information used for analysis was total number of machines, memory, execution time, etc. So, the output of the TLBO process sequence was used as training input for the Error Back Propagation Neural Network for learning. This trained neural network improved the work job sequence quality. Training was done in such a way that all sets of features were utilized to pair with their process requirement and current position. For increasing the reliability of the work, an experiment was done on a real dataset. Results show that the proposed model has overcome various evaluation parameters on a different scale as compared to previous approaches adopted by researchers.

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