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Relevant bibliographies by topics / Neural networks (Computer science) Statistical mechanics. Combination optimization

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Academic literature on the topic 'Neural networks (Computer science) Statistical mechanics. Combination optimization'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Neural networks (Computer science) Statistical mechanics. Combination optimization"

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Li, Y., R. L. Mahajan, and N. Nikmanesh. "Fine Pitch Stencil Printing Process Modeling and Optimization." Journal of Electronic Packaging 118, no. 1 (1996): 1–6. http://dx.doi.org/10.1115/1.2792121.

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In this paper, we present a statistical-neural network modeling approach to process optimization of fine pitch stencil printing for solder paste deposition on pads of printed circuit boards (PCB). The overall objective was to determine the optimum settings of the design parameters that would result in minimum solder paste height variation for the new board designs with 20-mil, 25-mil, and 50-mil pitch pad patterns. As a first step, a Taguchi orthogonal array, L27, was designed to capture the main effects of the six important printing machinery parameters and the PCBs pad conditions. Some of th

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Li, J., and K. C. Gupta. "Mechanism Design with MP-Neural Networks." Journal of Mechanical Design 120, no. 4 (1998): 527–32. http://dx.doi.org/10.1115/1.2829310.

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The prevalent Mathematical Programming Neural Network (MPNN) models are surveyed, and MPNN models have been developed and applied to the unconstrained optimization of mechanisms. Algorithms which require Hessian inversion and those which build up a variable approach matrix, are investigated. Based upon a comprehensive investigation of the Augmented Lagrange Multiplier (ALM) method, new algorithms have been developed from the combination of ideas from MPNN and ALM methods and applied to the constrained optimization of mechanisms. A relationship between the weighted least square minimization of

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Fang, Yin-Ying, Chi-Fang Chen, and Sheng-Ju Wu. "Feature identification using acoustic signature of Ocean Researcher III (ORIII) of Taiwan." ANZIAM Journal 59 (July 25, 2019): C318—C357. http://dx.doi.org/10.21914/anziamj.v59i0.12655.

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Underwater acoustic signature identification has been employed as a technique for detecting underwater vehicles, such as in anti-submarine warfare or harbour security systems. The underwater sound channel, however, has interference due to spatial variations in topography or sea state conditions and temporal variations in water column properties, which cause multipath and scattering in acoustic propagation. Thus, acoustic data quality control can be very challenging. One of challenges for an identification system is how to recognise the same target signature from measurements under different te

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Sabir, Zulqurnain, Ag Asri Ag Ibrahim, Muhammad Asif Zahoor Raja, et al. "Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model." Applied Sciences 11, no. 18 (2021): 8549. http://dx.doi.org/10.3390/app11188549.

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The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the

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A., Chandrashekar, B. V. Chaluvaraju, Asif Afzal, et al. "Mechanical and Corrosion Studies of Friction Stir Welded Nano Al2O3 Reinforced Al-Mg Matrix Composites: RSM-ANN Modelling Approach." Symmetry 13, no. 4 (2021): 537. http://dx.doi.org/10.3390/sym13040537.

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Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SE

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Suwa, Tohru, and Hamid Hadim. "Multidisciplinary Placement Optimization of Heat Generating Electronic Components on Printed Circuit Boards." Journal of Electronic Packaging 129, no. 1 (2006): 90–97. http://dx.doi.org/10.1115/1.2429715.

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A multidisciplinary placement optimization methodology for heat generating electronic components on printed circuit boards (PCBs) is presented. The methodology includes thermal, electrical, and placement criteria involving junction temperature, wiring density, line length for high frequency signals, and critical component location which are optimized simultaneously using the genetic algorithm. A board-level thermal performance prediction methodology which is based on a combination of a superposition method and artificial neural networks is developed for this study. Two genetic algorithms with

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Bohari, Z. H., M. Isa, A. Z. Abdullah, P. J. Soh, and M. F. Sulaima. "A smart partial discharge classification SOM with optimized statistical transformation feature." Bulletin of Electrical Engineering and Informatics 10, no. 2 (2021): 1054–62. http://dx.doi.org/10.11591/eei.v10i2.2751.

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Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised

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P, Vijaya, and Binu D. "Introduction to the Special Issue on Intelligence on Scalable computing for Recent Applications." Scalable Computing: Practice and Experience 21, no. 2 (2020): 157–58. http://dx.doi.org/10.12694/scpe.v21i2.1581.

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The special issue has been focussed to overcome the challenges of scalability, which includes size scalability, geographical scalability, administrative scalability, network and synchronous communication limitation, etc.The challenges also emerge with the development of recent applications. Hence this proposal has been planned to handle the scalability issues in recent applications. This special issue invites researchers, engineers, educators, managers, programmers, and users of computers who have particular interests in parallel processing and/or distributed computing and artificial intellige

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Dissertations / Theses on the topic "Neural networks (Computer science) Statistical mechanics. Combination optimization"

1

Morabito, David L. "Statistical mechanics of neural networks and combinatorial opimization problems /." Online version of thesis, 1991. http://hdl.handle.net/1850/11089.

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