Výsledky vyhledávání

Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

Autor: N. Leema, Arputharaj Kannan, V. R. Elgin Christo, Khanna H. Nehemiah

Publikováno v: International Journal of Operations Research and Information Systems. 11:62-85

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriat

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a208e9f2e32c830a8e67607195ae7d10
https://doi.org/10.4018/ijoris.2020100104

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Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

Autor: N. Leema, Arputharaj Kannan, H. Khanna Nehemiah

Publikováno v: International Journal of Operations Research and Information Systems. 9:32-52

In this article, a classification framework that uses quantum-behaved particle swarm optimization neural network (QPSONN) classifiers for diagnosing a disease is discussed. The neural network used for classification is radial basis function neural ne

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a01af988baebbafbe078c96573fdf65a
https://doi.org/10.4018/ijoris.2018040102

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Quantum-Behaved Particle Swarm Optimization Based Radial Basis Function Network for Classification of Clinical Datasets

Autor: N. Leema, H. Khanna Nehemiah, A. Kannan

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::6f6b6ee18c12cfa4995d9e16a0621c99
https://doi.org/10.4018/978-1-7998-2460-2.ch065

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Neural network classifier optimization using Differential Evolution with Global Information and Back Propagation algorithm for clinical datasets

Autor: Arputharaj Kannan, N. Leema, H. Khanna Nehemiah

Publikováno v: Applied Soft Computing. 49:834-844

Display Omitted A CAD system for disease diagnosis using Differential Evolution with Global Information (DEGI) based Back Propagation (BP) is proposed.The CAD system is trained and tested using six benchmark dataset.DEGI overcomes the premature conve

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::71dd5659658960b34073a598a8fdb4ab
https://doi.org/10.1016/j.asoc.2016.08.001

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Characterization, pore size measurement and wear model of a sintered Cu–W nano composite using radial basis functional neural network

Autor: P. Radha, N. Leema, S.C. Vettivel, H. Khanna Nehemiah

Publikováno v: Materials & Design. 68:195-206

Cu–(5–20%) W composite preforms, with a density of 94% were prepared through mechanical milling, mixing, compaction, sintering and hot extrusion. The X-ray Diffraction analysis, Particle Size analysis, Transmission Electron Microscope, Scanning E

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5cd51ab5fa159936d68c294f53662377
https://doi.org/10.1016/j.matdes.2014.11.035

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Electrical resistivity, wear map and modeling of extruded tungsten reinforced copper composite

Autor: S.C. Vettivel, N. Selvakumar, A. Haiter Lenin, N. Leema

Publikováno v: Materials & Design (1980-2015). 56:791-806

The present work includes the effect of W addition in improving the properties of Cu with the help of high-energy mechanical alloying. Composite preforms of (5–15 W) with copper were fabricated by Powder Metallurgy (P/M) method. The preforms were s

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a77068fba859562111a9560a1ad32dc4
https://doi.org/10.1016/j.matdes.2013.11.070

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