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An Efficient Approach of Artificial Neural Network in Runoff Forecasting

Satanand Mishra, Prince Gupta, S. K. Pandey, J. P. Shukla
2014 International Journal of Computer Applications  
The long-term and short-term forecasting model was developed for runoff forecasting using various approaches of Artificial Neural Network techniques.  ...  This study compares various approaches available for runoff forecasting of artificial neural networks (ANNs).  ...  Wavelet Neural Network Based Approach A Wavelet Neural Network Based Approach is the most efficient approach used for hydrological prediction and the main difference from other is that it combines two  ... 
doi:10.5120/16003-4991 fatcat:r4d2vpehofeezpelbb2hvljsai

Multichannel Time-Series Modelling And Prediction By Wavelet Networks

A. Prochazka, J.H. Smith
1996 Zenodo  
The analysis provided in this paper is devoted to the use of wavelet functions in the rst layer of neural networks using an algorithmic approach presented in Fig. 3 .  ...  Another application of wavelet functions is in their use as transfer functions in neural networks for signal modelling and prediction.  ... 
doi:10.5281/zenodo.36107 fatcat:x3frnsb2zzdw5pihnebd6wpvta

Information systems based on neural network and wavelet methods with application to decision making, modeling and prediction tasks

D.A. Karras, S.A. Karkanis, B.G. Mertzios
1998 Kybernetes  
), telecommunications and data transmission involve modeling of signals and images (e.g. for their efficient compression) and finally, time series forecasting involves prediction of signal fluctuations  ...  For instance, medical diagnosis and quality control involve decision making (deciding, using signal and image information, whether a person suffers from an illness or a product should be rejected as useless  ...  Information processing using neural networks In both information systems we employ a neural network technique for either classification or function approximation.  ... 
doi:10.1108/03684929810209405 fatcat:ottqibci5fbzhhtlehbiradbfa

Classification of Cardiovascular Disease from ECG using Artificial Neural Network and Hidden Markov Model

Mr. Ankit Sanghavi, Prof. Sachin M. Bojewar
2014 IOSR Journal of Computer Engineering  
Artificial neural network (ANN) is used as classifier with wavelet transform as the feature extraction for reducing data set of ECG.  ...  Hidden markov model (HMM) is used as predictor along with artificial neural network (ANN). ECG samples are collected for testing from MIT_BIH database.  ...  Fig.7.ANN training The backpropogation feed forward neural network is used for the proposed system.  ... 
doi:10.9790/0661-16349298 fatcat:xow3ed7uzvhdnlyody56ojga5u


2014 American Journal of Applied Sciences  
It differs from most existing approaches in its use of wavelet transform for generating different time scales for a signal and using these scales as an input to a two-stage neural network predictor.  ...  The primary contribution of our work would be to empirically evaluate the effectiveness of multi resolution analysis as an input to neural network prediction engine specifically for the purpose of intrusion  ...  To define the expected behavior our approach uses wavelet decomposed multi-scale time series signals as input to a two stage neural network for prediction of what value the observed variable should take  ... 
doi:10.3844/ajassp.2014.1405.1411 fatcat:k6zhdkztfva3rid7lk6ulz5cay

Fuzzy-Neural Network Traffic Prediction Framework with Wavelet Decomposition

Heng Xiao, Hongyu Sun, Bin Ran, Youngtae Oh
2003 Transportation Research Record  
This framework combined several artificial intelligence technologies such as wavelet transform, neural network, and fuzzy logic.  ...  In addition to developing the prediction framework, the wavelet de-noising method is also emphasized and analyzed in this paper.  ...  The neural network approaches have been commonly used for the traffic prediction problem during the past decade (Dougherty et al., 1994; Ledoux et al., 1997).  ... 
doi:10.3141/1836-03 fatcat:zgjwwfc7djebplgh2d6erknx5y

A Promising Wavelet Decomposition –NNARX Model to Predict Flood

Mohd Azrol Syafiee Anuar, Ribhan Zafira Abdul Rahman, Azura Che Soh, Samsul Bahari Mohd Noor, Zed Diyana Zulkafli
2020 International journal of electrical and computer engineering systems  
This research aimed to improve the performance of the neural network model for flood prediction.  ...  A new technique was proposed for modelling nonlinear data of flood forecasting using the wavelet decomposition-NNARX approach.  ...  This approach was used for optimal wavelet packet decomposition combined with the SVM to predict variable bit rate video traffic [27] .  ... 
doi:10.32985/ijeces.11.2.4 fatcat:azhhecp47vd2be53tqwa57lhh4

Sensor Fusion by Neural Network and Wavelet Analysis for Drill-Wear Monitoring

2010 Journal of Solid Mechanics and Materials Engineering  
The input features to the neural networks were extracted from acoustic emission (AE), vibration and current signals using the wavelet packet transform (WPT) analysis.  ...  The results indicated that the supervised neural networks were effective for drill-wear monitoring and the output of the neural networks can be directly utilized for the planning of tool life management  ...  Out of 483 patterns, 438 patterns are chosen randomly for training the network and others for testing (simulating) the neural network.  ... 
doi:10.1299/jmmp.4.749 fatcat:skbdmxdc6na3xi7kqmlnqp3nxe

Short Term Load Forecasting for Erbil Distribution System Using ANN and Wavelet Transform

A. A. Rasool, A. A. Fttah, I.B.S adik
2009 International Journal of Computer and Electrical Engineering  
In this paper an approach is proposed for Short Term Load Forecasting (STLF) which combines Wavelet Transform (WT) and Artificial Neural Network (ANN).  ...  Feed Forward Neural Networks are trained by low frequencies and corresponding average temperature or maximum and minimum temperature to predict the approximation part for the next seven days.  ...  Feed Forward Neural Network Neural Networks have been used in a broad range of applications including: Pattern Recognition, Optimization, Prediction and automatic control.  ... 
doi:10.7763/ijcee.2009.v1.53 fatcat:2n42gwazune3hmeoqqzy5wsoz4

Human lower extremity joint moment prediction: A wavelet neural network approach

Marzieh Mostafavizadeh Ardestani, Xuan Zhang, Ling Wang, Qin Lian, Yaxiong Liu, Jiankang He, Dichen Li, Zhongmin Jin
2014 Expert systems with applications  
First, a wavelet neural network was developed for the first time in this study to address the disadvantages of the traditional neural network .WNN predicted joint moments more accurately than feed forward  ...  Accordingly WNN could not be more accurate than For the future application, wavelet neural network can be employed in conjunction with inverse dynamics analysis to decrease the computational cost.  ... 
doi:10.1016/j.eswa.2013.11.003 fatcat:oymvuprw2rdhjnetyjgnwsmgxu

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Sonia Sunny, David Peter S., K. Poulose Jacob
2012 2012 International Conference on Advances in Computing and Communications  
First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural  ...  Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks.  ...  Since neural networks are good at pattern recognition, many early researchers applied neural networks for speech pattern recognition.  ... 
doi:10.1109/icacc.2012.7 fatcat:tuikml3exfb6vku6kzlunshk6e

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

Khaled A. Althelaya, Salahadin A. Mohammed, El-Sayed M. El-Alfy
2021 IEEE Access  
In this article, we propose a model based on deep neural networks that improves the forecasting of stock prices.  ...  Recently, however, deep neural network has been found to be more efficient than those in many application domains.  ...  ACKNOWLEDGMENT The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, for support during this work.  ... 
doi:10.1109/access.2021.3051872 fatcat:vdlnsq66bzfxthrnsprds6bwgq

The Use of Dual-Tree Complex Wavelet Transform (DTCWT) Based Feature for Mammogram Classification

Lowis, Hendra, Lavinia
2015 International Journal of Signal Processing, Image Processing and Pattern Recognition  
In this study, we propose the use of Dual-Tree Complex Wavelet Transform (DTCWT) based feature with neural network classifier for mammography image analysis.  ...  Jasmine, Govardhan, and Baskaran propose a method for detecting a micro calcification in digital mammograms using wavelet analysis by applying a combination of artificial neural network (ANN) for the classifier  ...  ., Dr for his guidance during the course so we can finish this study.  ... 
doi:10.14257/ijsip.2015.8.3.08 fatcat:dpq4m4tm6nfx7nrz35jozcpoge


Jarosław Smoczek
2015 Journal of KONES Powertrain and Transport  
The artificial intelligence is frequently addressed to the predictive problem by utilizing the learning capability of artificial neural network (ANN), and possibility of nonlinear mapping using fuzzy rules-based  ...  The paper is a survey of intelligent methods for failure prediction, and delivers the review of examples of scientific works presenting the computational intelligence-based approaches to predictive problem  ...  In [30] Authors propose the genetic approach for optimizing the dilatation and translation coefficients of a wavelet network used for time series prediction.  ... 
doi:10.5604/12314005.1138154 fatcat:rpenk3etgbc2dgsza6ula6otwq

Neurocomputing in Civil Infrastructure

Juan P. Amezquita-Sanchez, Martin Valtierra-Rodriguez, Mais Aldwaik, Hojjat Adeli
2016 Scientia Iranica. International Journal of Science and Technology  
The most common ANN used in structural engineering is the backpropagation neural network followed by recurrent neural networks and radial basis function neural networks.  ...  In recent years, a number of researchers have used newer hybrid techniques in structural engineering such as the neuro-fuzzy inference system, time-delayed neuro-fuzzy inference system, and wavelet neural  ...  Dai and Wang [89] presented an adaptive wavelet frame neural network method for ecient reliability analysis.  ... 
doi:10.24200/sci.2016.2301 fatcat:f35gtppgofaojkbertgsowsi24
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