Resilient back-propagation approach in small-world feed-forward neural network topology based on Newman–Watts algorithm

2020 ◽  
Vol 32 (20) ◽  
pp. 16279-16289 ◽  
Author(s):  
Okan Erkaymaz
Keyword(s):  
Neural Network ◽  
Network Topology ◽  
Back Propagation ◽  
Small World ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Resilient Back Propagation

scholarly journals Temporal solar irradiance variability analysis using neural networks

2015 ◽  
Vol 11 (S320) ◽  
pp. 333-338
Author(s):  
Ambelu Tebabal ◽  
Baylie Damtie ◽  
Melessew Nigussie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Solar Irradiance ◽  
Dominant Role ◽  
Back Propagation ◽  
Total Solar Irradiance ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Surface Magnetism ◽  
Feed Forward

AbstractA feed-forward neural network which can account for nonlinear relationship was used to model total solar irradiance (TSI). A single layer feed-forward neural network with Levenberg-marquardt back-propagation algorithm have been implemented for modeling daily total solar irradiance from daily photometric sunspot index, and core-to-wing ratio of Mg II index data. In order to obtain the optimum neural network for TSI modeling, the root mean square error (RMSE) and mean absolute error (MAE) have been taken into account. The modeled and measured TSI have the correlation coefficient of about R=0.97. The neural networks (NNs) model output indicates that reconstructed TSI from solar proxies (photometric sunspot index and Mg II) can explain 94% of the variance of TSI. This modeled TSI using NNs further strengthens the view that surface magnetism indeed plays a dominant role in modulating solar irradiance.

Prediction of Syllable Duration Using Structure Optimised Cuckoo Search Neural Network (SOCNN) for Text-To-Speech

2016 ◽  
Vol 13 (10) ◽  
pp. 7538-7544
Author(s):  
T Jayasankar ◽  
J. Arputha Vijayaselvi
Keyword(s):  
Neural Network ◽  
Speech Synthesis ◽  
Unit Length ◽  
Back Propagation ◽  
Cuckoo Search ◽  
National Language ◽  
Text To Speech ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
The Neural Network

A Feed Forward Neural Network (FFNN) model primarily based unrestricted delivery prediction of language unit length pattern info speech synthesis system is that the focus of this paper. Estimation of delivery parameter of segmental length plays a essential half in unrestricted concatenative synthesis Text To Speech System (TTS) is capable of synthesize natural sounding speech with improved quality. Common options to coach the Neural Network enclosed language unit position within the phrase, context of language unit, language unit position within the word, language unit nucleus and amp; language unit identity square measure extracted from the text. Back-propagation Neural Network (BPNN) formula is one in every of the foremost wide used and a preferred technique to optimize the feed forward neural network coaching in delivery prediction. For enhance the accuracy of delivery prediction language unit length in neural BP, that’s Cuckoo Search formula to seek out the structure of the neural network with least weights while not compromising on the prediction error is planned. Speech information is adopted to check the length prediction performance of planned SOCNN, wherever the obtained results demonstrate a marked improvement over the essential BP. The system performance is shown mistreatment the synthesizing natural sounding speech for Tamil, national language of Republic of India.

scholarly journals Design Feed Forward Neural Network to Solve Singular Boundary Value Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Luma N. M. Tawfiq ◽  
Ashraf A. T. Hussein
Keyword(s):  
Neural Network ◽  
Boundary Value Problems ◽  
Boundary Value ◽  
Back Propagation ◽  
Singular Boundary ◽  
Training Algorithms ◽  
Singular Boundary Value Problems ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
The Neural Networks

The aim of this paper is to design feed forward neural network for solving second-order singular boundary value problems in ordinary differential equations. The neural networks use the principle of back propagation with different training algorithms such as quasi-Newton, Levenberg-Marquardt, and Bayesian Regulation. Two examples are considered to show that effectiveness of using the network techniques for solving this type of equations. The convergence properties of the technique and accuracy of the interpolation technique are considered.

scholarly journals Automatic Flood Detection in Multi-Temporal Sentinel-1 Synthetic Aperture Radar Imagery Using ANN Algorithms

2020 ◽  
Vol 15 (3) ◽  
Author(s):  
Anusha Nallapareddy ◽  
Bharathi Balakrishnan
Keyword(s):  
Neural Network ◽  
Synthetic Aperture Radar ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Synthetic Aperture ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Wide Range ◽  
Multi Temporal ◽  
Aperture Radar

Natural Calamities like floods cause wide-range of damage to human existence as well as substructures. For automatic extraction of flooded area in multi-temporal satellite imagery acquired by Sentinel-1 Synthetic Aperture Radar (SAR), this paper presents two neural network algorithms: Feed-Forward Neural Network, Cascade-forward back-propagation neural network. This work currently focuses on Uttar Pradesh in India, which was affected due to floods during August 2017. The two models are trained, validated and tested using MATLAB R2018b. The models are first trained using a variety of input data until the percentage of error with respect to water body detection is within an acceptable error limit. These models are then used to extract the water features effectively and to detect the flooded regions. Finally, flood area is calculated in sq. km in during flood and post-flood imagery using these algorithms. The results thus obtained are compared with that from the binary thresholding method from previous studies. The results show that the Feed- Forward Neural Network gives better accuracy than the Cascade-forward back propagation neural network. Based on the promising results, the proposed method may assist in our understanding of the role of machine learning in disaster detection.

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