scholarly journals DESIGN OF ARTIFICIAL NEURAL NETWORK SOFTWARE FOR PREDICTING THE HEALTH GRADE IN A TELEPHONE EXCHANGE

SAINTEKBU ◽  
2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Wiratmoko Yuwono ◽  
Yodik Iwan Herlambang ◽  
Mauridhi Hery Purnomo ◽  
Prima Kristalina
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Telephone Exchange ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Network Software

Application of artificial neural network software ( ANN ) has been implemented forpredicting many thing and replace the conventional ways of predicting method using linearregression. Back Propagation algorithm can be used to reach the result of the program thatcan predict the telephone exchange health grade according to the data that has beenrecorded before. By predicting each parameter that has correlation to the telephoneexchange health grade, we can predict the telephone exchange health grade in the nextperiod.Kata kunci : jaringan syaraf tiruan, propagasi balik, nilai kesehatan sentral.

Artificial Neural Network Modelling for Waste

2011 ◽  
pp. 224-263 ◽  
Author(s):  
Eldon R. Rene ◽  
M. Estefanía López ◽  
María C. Veiga ◽  
Christian Kennes
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Network Modelling ◽  
Waste Gas ◽  
Propagation Algorithm ◽  
Artificial Neural

Due to their inherent robustness, artificial neural network models have proven to be successful and have been used extensively in biological wastewater treatment applications. However, only recently, with the scientific advancements made in biological waste gas treatment systems, the application of neural networks have slowly gained the practical momentum for performance monitoring in this field. Simple neural models, after vigorous training and testing, are able to generalize the results of a wide range of operating conditions, with high prediction accuracy. This chapter gives a fundamental insight and overview of the process mechanism of different biological waste gas (biofilters, biotrickling filters, continuous stirred tank bioreactors and monolith bioreactors), and wastewater treatment systems (activated sludge process, trickling filter and sequencing batch reactors). The basic theory of artificial neural networks is explained with a clear understanding of the back propagation algorithm. A generalized neural network modelling procedure for waste treatment applications is outlined, and the role of back propagation algorithm network parameters is discussed. Anew, the application of neural networks for solving specific environmental problems is presented in the form of a literature review.

scholarly journals Prediction of Drape Coefficient by Artificial Neural Network

2015 ◽  
Vol 15 (4) ◽  
pp. 266-274 ◽  
Author(s):  
Adel Ghith ◽  
Thouraya Hamdi ◽  
Faten Fayala
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

An Improved Artificial Neural Network for Recognition of Beam Pump Dynamometer Card

2012 ◽  
Vol 524-527 ◽  
pp. 1331-1334
Author(s):  
Jun Ni ◽  
Zhan Li Ren ◽  
Guo Qing Han
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Linear Mapping ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Beam Pump ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Dynamometer Card

Beam pump dynamometer card plays an important role in identifying the production state of oil wells. With an ability to reflect any non-linear mapping relationship, the artificial neural network (ANN) can be used in pattern recognition. This paper illuminates ANN realization in identifying fault kinds of dynamometer cards, including a back-propagation algorithm, characteristics of the Dynamometer card and some examples. It is concluded that the buildup of a neural network and the abstract of dynamometer cards are important to successful application.

scholarly journals Recognition of Text CAPTCHA by using Back Propagation Algorithm of Artificial Neural network

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1448-1459
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Storage System ◽  
Back Propagation ◽  
Machine Learning Techniques ◽  
Security Level ◽  
Simple Type ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural

Text-based CAPTCHA is a very simple type of CAPTCHA which are most widely used. It uses only a group of characters. In this paper, we focus on how Text based CAPTCHA is recognized by machine learning techniques. This paper proposed a method based on Back Propagation algorithm to identify the Text based CAPTCHA. The proposed technique improves the security level of Text-based CAPTCHA storage system by using the Back-propagation method of Artificial Neural Network. We used NNToolbox to train the network in MATLAB software

scholarly journals Voice Identity Finder Using the Back Propagation Algorithm of an Artificial Neural Network

2016 ◽  
Vol 95 ◽  
pp. 245-252 ◽  
Author(s):  
Roger Achkar ◽  
Mustafa El-Halabi ◽  
Elie Bassil ◽  
Rayan Fakhro ◽  
Marny Khalil
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Propagation Algorithm ◽  
Artificial Neural
Sign in / Sign up

Export Citation Format

Share Document