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Multi-mother wavelet neural network based on genetic algorithm and multiresoluion analysis for fast 3d mesh deformation
2019
IET Image Processing
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The current study presents a new 3D mesh deformation process using multi-mother wavelet neural network architecture, which relies on genetic algorithm and multiresolution analysis. ...
The experimental results showed the validity of the generalisation ability and the efficiency of their suggested multi-mother wavelet network architecture based on genetic algorithm and multiresolution ...
Our method relies on multi-mother wavelet neural network structure applying the genetic algorithm and multi-resolution analysis (MMWNN-GA-MA) based on different mother wavelets families (MMWNN) for the ...
doi:10.1049/iet-ipr.2019.0343
fatcat:7lvehtqnnfg5tiaxzoxmgxvod4
A New Algorithm for Initialization and Training of Beta Multi-Library Wavelets Neural Network
[chapter]
2008
Advances in Robotics, Automation and Control
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In this chapter, we propose a new algorithm of wavelets networks training, based on gradient that requires: • A set of training examples: the wavelets networks are parametrables functions, used to achieve ...
cost function that measures the gap between the input of the wavelets network and the desired output (in the case of classification) or the measured values (in case of modeling) present on the set of training ...
New Algorithm for Initialization and Training of Beta Multi-Library Wavelets Neural Network
www.intechopen.com ...
doi:10.5772/5528
fatcat:yut6wvv5gnfobkmkhayounphpa
Image Recognition Using Combination of Discrete Multi_Wavelet and Wavenet Transform
2008
American Journal of Applied Sciences
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In this method, the resulting coefficients were computed by the proposed multi-wavelets transform for single-level decomposition. ...
This method is based on using the combination of the Discrete Multi-wavelet Transform (DMWT) and Wavenet Transform (WN). ...
Step 2: Input the initial values of wavelet network parameters, which are: a. The dilation (a) of the mother wavelet. b. The weight (w) of the mother wavelet. c. ...
doi:10.3844/ajassp.2008.418.426
fatcat:6o5zkbvul5etjdydlblnax4s2u
Wavelet support vector machine and multi-layer perceptron neural network with continues wavelet transform for fault diagnosis of gearboxes
2017
Journal of Vibroengineering
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In this paper, a method based on wavelet support vector machine (SVM) with OAOT algorithm, multi-layer perceptron (MLP) and Morlet wavelet transform were designed to diagnose different types of fault in ...
Moreover, energy and entropy of the wavelet coefficients are used as two new features along with other statistical parameters as input of the classifier. ...
We construct wavelet SVM using four wavelet functions and then input training set into network and train the network. Mean squared error (MSE) is a SVM performance function. ...
doi:10.21595/jve.2016.16813
fatcat:mhcpwb6w3zfzbdexzzasuxafb4
Pattern Classification of Decomposed Wavelet Information using ART2 Networks for echoes Analysis
2008
Journal of Applied Research and Technology
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A thorough analysis between the neural network training and the type wavelets used for the training has been developed, showing that the Symlet 6 wavelet is the optimum for our problem. ...
for flaws detection and localization. ...
The procedure presented in here is divided in two stages. Firstly, a learning stage is performed by the use of wavelets and the ART2 network in a cascade procedure. ...
doi:10.22201/icat.16656423.2008.6.01.518
fatcat:jnses5zbnjaszb264lnbsrv6oi
SW-ELM: A summation wavelet extreme learning machine algorithm with a priori parameter initialization
2014
Neurocomputing
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Following that, the aim of this paper is to propose a new structure of connectionist network, the Summation Wavelet Extreme Learning Machine (SW-ELM) that enables good accuracy and generalization performances ...
and a multi-steps ahead prediction problem. ...
To initialize wavelet dilation and translation parameters (a k and b k in Eq. (4)) before the learning phase, a heuristic approach is applied to generate daughter wavelets from a mother wavelet function ...
doi:10.1016/j.neucom.2013.07.021
fatcat:zu2ortesvraanf2dgyl3ay6pla
A Wavelet Neural Network for SAR Image Segmentation
2009
Sensors
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This paper proposes a wavelet neural network (WNN) for SAR image segmentation by combining the wavelet transform and an artificial neural network. ...
The WNN combines the multiscale analysis ability of the wavelet transform and the classification capability of the artificial neural network by setting the wavelet function as the transfer function of ...
Acknowledgements The authors would like to thank the anonymous reviewers for their detailed comments and questions which improved the quality of the presentation of this paper. ...
doi:10.3390/s90907509
pmid:22400005
pmcid:PMC3290509
fatcat:frktu4sl3ffgzgx4qvbbh5faxq
A New Quantum Radial Wavelet Neural Network Model Applied to Analysis and Classification of EEG Signals
2014
International Journal of Computer Applications
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In this paper, a new model of multi-level transfer function radial wavelet neural network using quantum computing is achieved. ...
A new factor that combines the accuracy and the time of classification is suggested to evaluate the performance of the proposed model with other previous models. ...
This paper proposed a new quantum radial wavelet neural network (QRWNN) model. It combines Gaussian wavelet basis function with radial basis function and quantum neural network. ...
doi:10.5120/14851-3216
fatcat:h3lpr36cmfggbjaucjv56zbdjm
Wavelet-Network based on L1-Norm minimisation for learning chaotic time series
2005
Journal of Applied Research and Technology
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This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series. ...
The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in the hidden layer of the wavelet-network. ...
ACKNOWLEDGEMENTS This work was carried out during the first author stay at Imperial College London, UK, and was supported by the National Council for Science and Technology (CONACYT), MEXICO. ...
doi:10.22201/icat.16656423.2005.3.03.561
fatcat:2zjhqy6cp5aw3buh4j2mgopvoa
A wavelet-SARIMA-ANN hybrid model for precipitation forecasting
2016
Journal of Water and Land Development
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As monthly precipitation time series have nonlinear features and multiple time scales, wavelet, seasonal auto regressive integrated moving average (SARIMA) and hybrid artificial neural network (ANN) methods ...
Comparing model-generated values with observed data, the wavelet-SARIMA-ANN model was seen to outperform wavelet-ANN and wavelet-SARIMA models in terms of precipitation forecasting accuracy. ...
Acknowledgements Partial funding for this study was provided by an NSERC Discovery Grant held by Jan Adamowski. ...
doi:10.1515/jwld-2016-0003
fatcat:gf5jkzpu2vfjjlpnmhtinaztam
An Experimental Study on Speech Enhancement Based on a Combination of Wavelets and Deep Learning
2022
Computation
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The extensive experimentation performed to select the proper wavelets and the training of neural networks allowed us to assess whether the hybrid approach is of benefit or not for the speech enhancement ...
In this paper, we evaluate a hybrid approach that combines both deep learning and wavelet transformation. ...
With the purpose of performing a proper comparison, the same amount of epochs for training the deep neural networks was used for both (noisy, clean) and (wavelet-denoised, clean) procedures. ...
doi:10.3390/computation10060102
fatcat:rt62ehqj55a6jiyrg6pgp7hfvq
Fuzzy Wavenet (FWN) classifier for medical images
2005
˜Al-œKhawarizmi engineering journal
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They demonstrate a considerable improvement in performance by proposed two table's rule for fuzzy and deterministic dilation and translation in wavelet transformation techniques. ...
The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. ...
Furthermore, deciding on the optimal architecture and training procedure is often difficult, as stated above. ...
doaj:9ab82800222f475083bf3e3864d0d9b4
fatcat:7sakedirf5cwlbt7cgokxgcziq
A Gear Fault Identification using Wavelet Transform, Rough set Based GA, ANN and C4.5 Algorithm
2014
Procedia Engineering
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network algorithm and C4.5.Performance of classifiers are evaluated with the different signals acquired from the experimental test rig for different states of gears. ...
Signal processing categorized to time-frequency domain such as continues wavelet transform is used in the proposed work for statistical feature extraction. ...
Mother wavelet gives a source function to generate the translated and scaled version of its sibling wavelets. ...
doi:10.1016/j.proeng.2014.12.337
fatcat:iowpio4iojdjdjfv3drn55qwsa
A Hybrid Neuro–Wavelet Predictor for QoS Control and Stability
[chapter]
2013
Lecture Notes in Computer Science
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2014 pre-print arXiv:1406.3156v1 fatcat:cqex3tiyybfwbgygty4dszcwo4
We use wavelet analysis, providing compression and denoising on the observed time series of the amount of past user requests; and a recurrent neural network trained with observed data and designed so as ...
Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. ...
Acknowledgments This work has been supported by project PRISMA PON04a2 A/F funded by the Italian Ministry of University within PON 2007-2013 framework. ...
doi:10.1007/978-3-319-03524-6_45
fatcat:z5jg2cl4cfbvvetcz732mre3yy
Time Series Modeling of River Flow Using Wavelet Neural Networks
2011
Journal of Water Resource and Protection
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A new hybrid model which combines wavelets and Artificial Neural Network (ANN) called wavelet neural network (WNN) model was proposed in the current study and applied for time series modeling of river ...
The observed time series are decomposed into sub-series using discrete wavelet transform and then appropriate sub-series is used as inputs to the neural network for forecasting hydrological variables. ...
In this paper, a new hybrid model called wavelet neural network model (WNN), which is the combination of wavelet analysis and ANN, has been proposed. ...
doi:10.4236/jwarp.2011.31006
fatcat:cqtdrb7zn5exlgwe2rkptwpfzi
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