6A-1 Design of LCR Filters Using Non-Linear Optimization Applied to Simplified Green Function Models

2006 ◽  
Author(s):  
R. C. Peach ◽  
Z. Xu
Keyword(s):  
Green Function ◽  
Linear Optimization ◽  
Non Linear ◽  
Function Models

Adsorption of ferrocyanide onto raw and acid-activated clinoptilolite and sepiolite: equilibrium modelling by error minimization

Clay Minerals ◽  
2013 ◽  
Vol 48 (4) ◽  
pp. 613-626 ◽  
Author(s):  
V. Önen ◽  
E. Yel
Keyword(s):  
Linear Models ◽  
Error Function ◽  
Linear Optimization ◽  
Error Minimization ◽  
Non Linear ◽  
Initial Adsorption ◽  
Adsorption Rates ◽  
Function Models ◽  
Best Fit ◽  
Modelling Approach

AbstractThe experimental data on adsorption of Fe and CN of a ferrocyanide complex onto raw and acid-activated clinoptilolite/sepiolite on the basis of detention time and particle size was modelled by a linear and a non-linear approach. The linearized best-fit isotherm selection method and non-linear error minimization was applied through Freundlich, Langmuir and Temkin isotherms. ERRSQ, MPSD, HYBRID and ARE error functions were minimized by a developed MATLAB script to determine the isotherm parameters in non-linear optimization. The complex was not adsorbed as whole anions but the Fe and CN were adsorbed separately. 0.65 mg Fe/L. min and 4.84 mg CN/L. min initial adsorption rates were achieved with acid activated clinoptilolite. The Fe adsorption was not as successful as CN. The adsorption of Fe and CN was described by Freundlich and Langmuir isotherms respectively. The differences between the predicted isotherm parameter sets of linear models and minimized error function models indicated that both the best-fit isotherm selection and the isotherm constant determinations can be performed properly by error minimization as well as by conventional linear best fit modelling approach.

scholarly journals Design of a New Synthetic Acceptance Sampling Plan

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 653 ◽  
Author(s):  
Saeed Dobbah ◽  
Muhammad Aslam ◽  
Khushnoor Khan
Keyword(s):  
Standard Deviation ◽  
Normal Distribution ◽  
Operating Characteristic ◽  
Linear Optimization ◽  
Sampling Plan ◽  
Quality Characteristic ◽  
Acceptance Sampling ◽  
Acceptance Sampling Plan ◽  
Non Linear

In this paper, we propose a new synthetic sampling plan assuming that the quality characteristic follows the normal distribution with known and unknown standard deviation. The proposed plan is given and the operating characteristic (OC) function is derived to measure the performance of the proposed sampling plan for some fixed parameters. The parameters of the proposed sampling plan are determined using non-linear optimization solution. A real example is added to explain the use of the proposed plan by industry.

scholarly journals Hybrid Genetic Algorithm and Simulated Annealing for Function Optimization

2017 ◽  
Vol 1 (2) ◽  
pp. 82 ◽  
Author(s):  
Tirana Noor Fatyanosa ◽  
Andreas Nugroho Sihananto ◽  
Gusti Ahmad Fanshuri Alfarisy ◽  
M Shochibul Burhan ◽  
Wayan Firdaus Mahmudy
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Hybrid Genetic Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Non Linear ◽  
Speed Up ◽  
Linear Problems

The optimization problems on real-world usually have non-linear characteristics. Solving non-linear problems is time-consuming, thus heuristic approaches usually are being used to speed up the solution’s searching. Among of the heuristic-based algorithms, Genetic Algorithm (GA) and Simulated Annealing (SA) are two among most popular. The GA is powerful to get a nearly optimal solution on the broad searching area while SA is useful to looking for a solution in the narrow searching area. This study is comparing performance between GA, SA, and three types of Hybrid GA-SA to solve some non-linear optimization cases. The study shows that Hybrid GA-SA can enhance GA and SA to provide a better result

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