scholarly journals FSD-HSO Optimization Algorithm for Closed Fringes Interferogram Demodulation

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ulises H. Rodriguez-Marmolejo ◽  
Miguel Mora-Gonzalez ◽  
Jesus Muñoz-Maciel ◽  
Tania A. Ramirez-delreal
Keyword(s):  
Relative Error ◽  
Optimization Technique ◽  
Harmony Search ◽  
Mathematical Optimization ◽  
Physical Nature ◽  
Algorithm Optimization ◽  
Interference Phenomenon ◽  
Sinusoidal Modulation ◽  
Search Optimization ◽  
Mathematical Optimization Model

Due to the physical nature of the interference phenomenon, extracting the phase of an interferogram is a known sinusoidal modulation problem. In order to solve this problem, a new hybrid mathematical optimization model for phase extraction is established. The combination of frequency guide sequential demodulation and harmony search optimization algorithms is used for demodulating closed fringes patterns in order to find the phase of interferogram applications. The proposed algorithm is tested in four sets of different synthetic interferograms, finding a range of average relative error in phase reconstructions of 0.14–0.39 rad. For reference, experimental results are compared with the genetic algorithm optimization technique, obtaining a reduction in the error up to 0.1448 rad. Finally, the proposed algorithm is compared with a very known demodulation algorithm, using a real interferogram, obtaining a relative error of 1.561 rad. Results are shown in patterns with complex fringes distribution.

scholarly journals Real-time implementation of an enhanced nonlinear PID controller based on harmony search for one-stage servomechanism system

2018 ◽  
Vol 12 (4) ◽  
pp. 4161-4179
Author(s):  
Mohamed. A. Shamseldin ◽  
Mohamed Sallam ◽  
A. M. Bassiuny ◽  
A. M. Abdel Ghany
Keyword(s):  
Real Time ◽  
Pid Controller ◽  
Controller Design ◽  
Optimization Technique ◽  
Harmony Search ◽  
Reference Test ◽  
Variable Position ◽  
Labview Software ◽  
One Stage ◽  
Search Optimization

This paper presents a real-time implementation of an enhanced nonlinear PID (NPID) controller to follow a preselected position profile of one stage servomechanism drive system. This purpose should be realized regardless the different operating points and external disorders (friction and backlash). In this study, the MATLAB Simulink used for purpose of controller design while the result from simulation will be executed in real time using LABVIEW software. There is not enough information about the servomechanism experimental setup so, the system identification techniques will be used via collecting experimental input/output data. The optimum parameters for the controllers have been obtained via harmony search optimization technique according to an effective cost function. Also, the performance of enhanced NPID controller has been investigated by comparing it with linear PID controller.  The experimental and simulation results show that the proposed NPID controller has minimum rise time and settling time through constant position reference test. Also, the NPID control is faster than the linear PID control by 40% in case of variable position reference test.

scholarly journals A modified simplex based direct search optimization algorithm for adaptive transversal FIR filters

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110254
Author(s):  
Armaghan Mohsin ◽  
Yazan Alsmadi ◽  
Ali Arshad Uppal ◽  
Sardar Muhammad Gulfam
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Fast Convergence ◽  
Good Convergence ◽  
Optimum Point ◽  
Search Optimization ◽  
Higher Dimensional ◽  
Gradient Based ◽  
Improved Accuracy ◽  
Quadratic Cost Function

In this paper, a novel modified optimization algorithm is presented, which combines Nelder-Mead (NM) method with a gradient-based approach. The well-known Nelder Mead optimization technique is widely used but it suffers from convergence issues in higher dimensional complex problems. Unlike the NM, in this proposed technique we have focused on two issues of the NM approach, one is shape of the simplex which is reshaped at each iteration according to the objective function, so we used a fixed shape of the simplex and we regenerate the simplex at each iteration and the second issue is related to reflection and expansion steps of the NM technique in each iteration, NM used fixed value of [Formula: see text], that is, [Formula: see text]  = 1 for reflection and [Formula: see text]  = 2 for expansion and replace the worst point of the simplex with that new point in each iteration. In this way NM search the optimum point. In proposed algorithm the optimum value of the parameter [Formula: see text] is computed and then centroid of new simplex is originated at this optimum point and regenerate the simplex with this centroid in each iteration that optimum value of [Formula: see text] will ensure the fast convergence of the proposed technique. The proposed algorithm has been applied to the real time implementation of the transversal adaptive filter. The application used to demonstrate the performance of the proposed technique is a well-known convex optimization problem having quadratic cost function, and results show that the proposed technique shows fast convergence than the Nelder-Mead method for lower dimension problems and the proposed technique has also good convergence for higher dimensions, that is, for higher filter taps problem. The proposed technique has also been compared with stochastic techniques like LMS and NLMS (benchmark) techniques. The proposed technique shows good results against LMS. The comparison shows that the modified algorithm guarantees quite acceptable convergence with improved accuracy for higher dimensional identification problems.

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