Simulation of the mean zero-up-crossing wave period using artificial neural networks trained with a simulated annealing algorithm

2007 ◽  
Vol 12 (1) ◽  
pp. 22-33 ◽  
Author(s):  
Hamid Bazargan ◽  
Hamid Bahai ◽  
Farzad Aryana
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
Wave Period ◽  
The Mean ◽  
Annealing Algorithm ◽  
Artificial Neural

Simulation of the Mean-Zero-Up-Crossing Wave Period Using Artificial Neural Networks Trained With Simulated Annealing Algorithm

2006 ◽  
Author(s):  
H. Bazargan ◽  
H. Bahai ◽  
F. Aryana ◽  
S. F. Yasseri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
North East ◽  
The North ◽  
Wave Heights ◽  
Significant Wave Heights ◽  
Annealing Algorithm ◽  
Artificial Neural

The aim of this work is to simulate the 3-houly mean zero-up-crossing wave periods (Tzs) of the sea-states of a future period for a location in the North East Pacific. Seven multi-layer artificial neural networks (ANNs) were trained with simulated annealing algorithm. The output of each ANN was used for estimating each of the 7 parameters of a new distribution, described in Appendix A, called hepta-parameter spline proposed for the conditional distribution of the Tz given some significant wave heights and mean zero-up-crossing wave periods. After estimating the parameters of the conditional distributions, the Tzs have been forecasted from the hepta-parameter spline distributions corresponding to the Tzs of the period.

Optimal Training of Artificial Neural Networks to Forecast Power System State Variables

2014 ◽  
Vol 3 (1) ◽  
pp. 65-82 ◽  
Author(s):  
Victor Kurbatsky ◽  
Denis Sidorov ◽  
Nikita Tomin ◽  
Vadim Spiryaev
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Simulated Annealing ◽  
Power System ◽  
Simulated Annealing Algorithm ◽  
Active Power ◽  
Support Vector ◽  
State Variables ◽  
Annealing Algorithm ◽  
Artificial Neural

The problem of forecasting state variables of electric power system is studied. The paper suggests data-driven adaptive approach based on hybrid-genetic algorithm which combines the advantages of genetic algorithm and simulated annealing algorithm. The proposed method has two stages. At the first stage the input signal is decomposed into orthogonal basis functions based on the Hilbert-Huang transform. The genetic algorithm and simulated annealing algorithm are applied to optimal training of the artificial neural network and support vector machine at the second stage. The results of applying the developed approach for the short-term forecasts of active power flows in the electric networks are presented. The best efficiency of proposed approach is demonstrated on real retrospective data of active power flow forecast using the hybrid-genetic support vector machine algorithm.

Artificial Neural Networks Estimation for Thicknesses of Multilayer Nano-Scale Films

2019 ◽  
Vol 962 ◽  
pp. 41-48
Author(s):  
Tzong Daw Wu ◽  
Jiun Shen Chen ◽  
Ching Pei Tseng ◽  
Cheng Chang Hsieh
Keyword(s):  
Thin Films ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real Time ◽  
Film Growth ◽  
Mean Squared Error ◽  
Squared Error ◽  
Thin Film Optics ◽  
The Mean ◽  
Artificial Neural

This study presents a real-time method for determining the thickness of each layer in multilayer thin films. Artificial neural networks (ANNs) were introduced to estimate thicknesses from a transmittance spectrum. After training via theoretical spectra which were generated by thin-film optics and modified by noise, ANNs were applied to estimate the thicknesses of four-layer nanoscale films which were TiO2, Ag, Ti, and TiO2 thin films assembled sequentially on polyethylene terephthalate (PET) substrates. The results reveal that the mean squared error of the estimation is 2.6 nm2, and is accurate enough to monitor film growth in real time.

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