A new simulation of distributed mutual exclusion on neural networks

Bayat, Peyman; Ahmadi, Abbas; Kordi, A.

doi:10.1109/citisia.2008.4607340

Cited by 1 publication

(1 citation statement)

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“…At each iteration, the value of a predefined discriminant function for each node is calculated using an input vector chosen randomly from the training dataset. The node that has the largest value of the discriminant function, at each iteration, is stated as the winner of the competition process [50,51].…”

Section: Training Algorithmmentioning

confidence: 99%

An artificial neural network based approach for prediction the thermal conductivity of nanofluids

et al. 2020

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Thermal conductivity is an important thermophysical property of nanofluids in many practical heat transfer applications. In this study, a novel approach is proposed to predict the thermal conductivity of nanofluids under multiple operating parameters. The proposed approach may be extended to be used to other thermophysical properties of nanofluids. The Kohonen's self-organizing maps (SOM), as an unsupervised artificial neural network (ANN), is used to provide an accurate prediction tool for the problem in hand. Furthermore, SOM, similar to any ANN-based approach, can handle nonlinear and complex input-output relationships with high generalization ability. Comparison of the SOM predicted values with corresponding available theoretical results as well as experimental data implies high prediction capability of the developed approach. The proposed approach was utilized to predict thermal conductivity ratio of oxide (Al 2 O 3 , CuO and TiO 2)/ water nanofluids under various operating conditions (nanoparticle size, temperature, and nanoparticle volume fraction). Keywords Nanofluids • Thermal conductivity • Artificial neural networks • Self-organizing maps List of symbols k Thermal conductivity (W/(m K)) T Temperature (°C) x Input vector w j Synaptic weight vector h ki Topological neighborhood d ik Euclidean distance r Thermal conductivity ratio Effective width of the topological neighborhood r Position vector Time constant P Projection matrix I g Identity matrix Volume fraction (%)

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Section: Training Algorithmmentioning

confidence: 99%