Fast Convergence in the Double Oral Auction

Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter

Applied Sciences ◽

10.3390/app11031211 ◽

2021 ◽

Vol 11 (3) ◽

pp. 1211

Author(s):

En-Chih Chang ◽

Chun-An Cheng ◽

Rong-Ching Wu

Keyword(s):

Steady State ◽

Digital Signal Processor ◽

Tracking Control ◽

Harmonic Distortion ◽

Digital Signal ◽

Fast Convergence ◽

Solution Quality ◽

Hybrid Strategy ◽

Optimal Tracking ◽

Optimal Tracking Control

This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.

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Achieving Low-Delay and Fast-Convergence in Stochastic Network Optimization

Proceedings of the 2016 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Science - SIGMETRICS '16 ◽

10.1145/2896377.2901474 ◽

2016 ◽

Cited By ~ 4

Author(s):

Jia Liu

Keyword(s):

Network Optimization ◽

Fast Convergence ◽

Stochastic Network ◽

Low Delay ◽

Stochastic Network Optimization

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

Science Progress ◽

10.1177/00368504211025409 ◽

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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A UMDA-Based Discretization Method for Continuous Attributes

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.403-408.1834 ◽

2011 ◽

Vol 403-408 ◽

pp. 1834-1838

Author(s):

Jing Zhao ◽

Chong Zhao Han ◽

Bin Wei ◽

De Qiang Han

Keyword(s):

Machine Learning ◽

Data Mining ◽

Evolutionary Algorithms ◽

Marginal Distribution ◽

Convergence Speed ◽

Fast Convergence ◽

Experimental Results ◽

Discretization Method ◽

Bottom Up ◽

Global Dynamic

Discretization of continuous attributes have played an important role in machine learning and data mining. They can not only improve the performance of the classifier, but also reduce the space of the storage. Univariate Marginal Distribution Algorithm is a modified Evolutionary Algorithms, which has some advantages over classical Evolutionary Algorithms such as the fast convergence speed and few parameters need to be tuned. In this paper, we proposed a bottom-up, global, dynamic, and supervised discretization method on the basis of Univariate Marginal Distribution Algorithm.The experimental results showed that the proposed method could effectively improve the accuracy of classifier.

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