scholarly journals Design of Speech Denoising Algorithm Based on Wavelet Threshold Function and PSO

2021 ◽  
Author(s):  
Lanyong Zhang ◽  
Ruixuan Zhang ◽  
Papavassiliou Christos
Keyword(s):  
Mathematical Modeling ◽  
Particle Swarm Optimization ◽  
Pso Algorithm ◽  
Threshold Function ◽  
Threshold Method ◽  
Swarm Optimization ◽  
Wavelet Filters ◽  
Bayesian Criterion ◽  
Speech Denoising ◽  
Simulation Results

At present, there are many shortcomings in the discontinuity of wavelet threshold function and the constant threshold of different decomposition layers and the constant error it produced. The amplitude-frequency characteristics of wavelet filters are studied and analyzed by mathematical modeling. An improved wavelet threshold function with adjustable parameters is proposed. Particle swarm optimization (PSO) algorithm is used to find the optimal parameters of the improved threshold function in a background noise environment. The improved wavelet threshold function is combined with Bayesian threshold method to obtain the threshold based on Bayesian criterion, which makes the threshold adaptive in different layers and overcomes the shortcomings of fixed threshold. Finally, the speech signal with optimal wavelet coefficients is obtained after reconstruction. Compared with the traditional threshold function, Simulation results show that the improved threshold function achieves precise notch denoising, effectively retains the singularity and eigenvalues of the signal, and reduces the signal distortion.

Convergence Analysis of Particle Swarm Optimization via Illustration Styles

2021 ◽  
pp. 29-37
Author(s):  
Wei-Der Chang ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Analysis ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Function Optimization ◽  
Evolutionary Computations ◽  
Swarm Optimization ◽  
Simulation Results ◽  
Population Based Algorithm

Particle swarm optimization (PSO) is the most important and popular algorithm to solving the engineering optimization problem due to its simple updating formulas and excellent searching capacity. This algorithm is one of evolutionary computations and is also a population-based algorithm. Traditionally, to demonstrate the convergence analysis of the PSO algorithm or its related variations, simulation results in a numerical presentation are often given. This way may be unclear or unsuitable for some particular cases. Hence, this paper will adopt the illustration styles instead of numeric simulation results to more clearly clarify the convergence behavior of the algorithm. In addition, it is well known that three parameters used in the algorithm, i.e., the inertia weight w, position constants c1 and c2, sufficiently dominate the whole searching performance. The influence of these parameter settings on the algorithm convergence will be considered and examined via a simple two-dimensional function optimization problem. All simulation results are displayed using a series of illustrations with respect to various iteration numbers. Finally, some simple rules on how to suitably assign these parameters are also suggested

A Method of Parameters Selecting in Robust Control Based on PSO Algorithm

2011 ◽  
Vol 320 ◽  
pp. 574-579
Author(s):  
Hua Li ◽  
Zhi Cheng Xu ◽  
Shu Qing Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Robust Control ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Satisfactory Performance ◽  
Novel Method ◽  
Simulation Results

Aiming at a kind of uncertainties of models in complex industry processes, a novel method for selecting robust parameters is stated in the paper. Based on the analysis, parameters selecting for robust control is reduced to be an object optimization problem, and the particle swarm optimization (PSO) is used for solving the problem, and the corresponding robust parameters are obtained. Simulation results show that the robust parameters designed by this method have good robustness and satisfactory performance.

scholarly journals Super-twisting sliding mode controllers based on D-PSO optimization for temperature control of an induction cooking system

2020 ◽  
Vol 11 (2) ◽  
pp. 1055
Author(s):  
Abdeldjalil Abdelkader Mekki ◽  
Abdelkader Kansab ◽  
Mohamed Matallah ◽  
Zinelaabidine Boudjema ◽  
Mouloud Feliachi
Keyword(s):  
Particle Swarm Optimization ◽  
Sliding Mode ◽  
Pso Algorithm ◽  
Sliding Mode Controller ◽  
Swarm Optimization ◽  
Fem Method ◽  
Cooking Temperature ◽  
Sliding Mode Controllers ◽  
Simulation Results ◽  
Induction Cooking

<p class="Default">In this study, we perform the control of the temperature evolution versus time of induction cooking system using a super twisting sliding mode control (STSMC) based on Dynamic Particle Swarm Optimization (D-PSO). First, we will determine the evolution of the temperature in the middle of the pan bottom using the FEM method. The found temperature exceeds the limit of the desired cooking temperature (150-200°C). Second, to limit temperature increase, a (ST-SMC) method combined with a (D-PSO) algorithm is used to get the desired temperature. Particles Swarm Optimization (D-PSO) method is used to optimize the parameters of the gain of (ST-SMC) and improve its performance. The simulation results show that the use of the optimized super twisting sliding mode controller helps to achieve a desired value of cooking.</p>

Multi-Objective Particle Swarm Optimization for Control Laws Design

2013 ◽  
Vol 333-335 ◽  
pp. 1361-1365
Author(s):  
Xiao Xiong Liu ◽  
Heng Xu ◽  
Yan Wu ◽  
Peng Hui Li
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Tracking Error ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Control Laws ◽  
Reference Models ◽  
Multi Objective ◽  
Simulation Results

In order to overcome the difficult of large amount of calculation and to satisfy multiple design indicators in the design of control laws, an improved multi-objective particle swarm optimization (PSO) algorithm was used to design control laws of aircraft. Firstly, the hybrid concepts of genetic algorithm were introduced to particle swarm optimization (PSO) algorithm to improve the algorithm. Then based on aircraft flying quality the reference models were built, and then the tracking error, settling time and overshoot were used as the optimization goal of the control laws design. Based on this multi-objective optimize problem the attitude hold control laws were designed. The simulation results show the effectiveness of the algorithm.

scholarly journals Enhancement transient stability of power system using UPFC with M-PSO

2020 ◽  
Vol 17 (1) ◽  
pp. 61
Author(s):  
Rashid H. AL-Rubayi ◽  
Luay G. Ibrahim
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Transient Stability ◽  
Electrical Power ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Simulation Results ◽  
The Stability

<span>During the last few decades, electrical power demand enlarged significantly whereas power production and transmission expansions have been brutally restricted because of restricted resources as well as ecological constraints. Consequently, many transmission lines have been profoundly loading, so the stability of power system became a Limiting factor for transferring electrical power. Therefore, maintaining a secure and stable operation of electric power networks is deemed an important and challenging issue. Transient stability of a power system has been gained considerable attention from researchers due to its importance. The FACTs devices that provide opportunities to control the power and damping oscillations are used. Therefore, this paper sheds light on the modified particle swarm optimization (M-PSO) algorithm is used such in the paper to discover the design optimal the Proportional Integral controller (PI-C) parameters that improve the stability the Multi-Machine Power System (MMPS) with Unified Power Flow Controller (UPFC). Performance the power system under event of fault is investigating by utilizes the proposed two strategies to simulate the operational characteristics of power system by the UPFC using: first, the conventional (PI-C) based on Particle Swarm Optimization (PI-C-PSO); secondly, (PI-C) based on modified Particle Swarm Optimization (PI-C-M-PSO) algorithm. The simulation results show the behavior of power system with and without UPFC, that the proposed (PI-C-M-PSO) technicality has enhanced response the system compared for other techniques, that since it gives undershoot and over-shoot previously existence minimized in the transitions, it has a ripple lower. Matlab package has been employed to implement this study. The simulation results show that the transient stability of the respective system enhanced considerably with this technique.</span>

Elimination of Lower Order Harmonics in Seven-Level Cascaded Inverters

2015 ◽  
Vol 793 ◽  
pp. 206-210
Author(s):  
Baharuddin Ismail ◽  
Syed Idris Syed Hassan ◽  
Rizalafande Che Ismail ◽  
Azralmukmin Azmi
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Equation ◽  
Lower Order ◽  
Pulse Width ◽  
Pulse Width Modulation ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Harmonic Elimination ◽  
Selective Harmonic Elimination ◽  
Simulation Results

This paper deals with an elimination of lower order harmonics in the seven - level cascaded inverters. The main objective of selective harmonic elimination pulse width modulation strategy is eliminating lower order harmonic by solving nonlinear equations, while the fundamental component is satisfied. In this paper, the Particle Swarm Optimization (PSO) is applied to a seven-level inverter for solving the nonlinear equation. With the proposed approach, the required switching angles are computed efficiently by PSO algorithm. Lower order harmonics up to the 7th are eliminated. Simulation results verify the proposed method.

scholarly journals Design of PSO Based Cascaded Multilevel Inverter with Unequal DC Sources

2018 ◽  
Vol 7 (4.38) ◽  
pp. 7
Author(s):  
Rupali Mohanty ◽  
Debashis Chatterjee ◽  
Gopinath Sengupta
Keyword(s):  
Particle Swarm Optimization ◽  
Output Voltage ◽  
Pso Algorithm ◽  
Transcendental Equation ◽  
Multilevel Inverter ◽  
Swarm Optimization ◽  
Harmonic Elimination ◽  
Simulation Results ◽  
Cascaded Multilevel Inverter ◽  
Elimination Problem

This paper focusing on, the total harmonics distortion (THD) of the output voltage is minimized by the help of a cascade multilevel inverter with non-equal DC sources using particle swarm optimization (PSO) algorithm.  The nonlinear transcendental equation that describing the harmonic elimination problem is solved by using many methods existing in literature. In the proposed technique, unequal DC sources are taken for the multilevel inverter, which is practical when different renewable sources are used. The desired switching angles are found out by implementing PSO algorithm which results the minimum THD. Experimental details along with simulation results of 11-level inverter are shown to validate the theory. 

A Study of Normalized Population Diversity in Particle Swarm Optimization

2020 ◽  
pp. 345-381
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Multimodal Function ◽  
The Matrix ◽  
Simulation Results ◽  
The Relationship ◽  
Solution Matrix

The values and velocities of a Particle swarm optimization (PSO) algorithm can be recorded as a series of matrix and its population diversity can be considered as an observation of the distribution of matrix elements. Each dimension is measured separately in the dimension-wise diversity. On the contrary, the element-wise diversity measures all dimensions together. In this chapter, the PSO algorithm is first represented in the matrix format. Then, based on the analysis of the relationship between pairs of vectors in the PSO solution matrix, different normalization strategies are utilized for dimension-wise and element-wise population diversity, respectively. Experiments on benchmark functions are conducted. Based on the simulation results of 10 benchmark functions (including unimodal/multimodal function, separable/non-separable function), the properties of normalized population diversities are analyzed and discussed.

Fault Diagnosis of Metro Vehicle Auxiliary Inverter Based on PSO-KPCM Algorithm

2013 ◽  
Vol 385-386 ◽  
pp. 593-596
Author(s):  
Zeng Tao Ma ◽  
Jun Wei Gao ◽  
Yong Qin ◽  
De Chen Yao ◽  
Bin Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Possibilistic Clustering ◽  
Clustering Center ◽  
Simulation Results ◽  
Membership Matrix

In this paper, the principle of kernel-based possibilistic clustering algorithm and particle swarm optimization algorithm are introduced and the application of the algorithms in fault diagnosis of auxiliary inverter is studied. Several common fault types are simulated by MATALB software. By initializing clustering center of samples based on PSO algorithm, calculating the final membership matrix and the final clustering center matrix based on the KPCM algorithm, the fault samples can be classified finally. The simulation results show that the PSO-KPCM algorithm can be used in the field of fault diagnosis. The PSO-KPCM algorithm even has better results and faster convergence rate than FCM algorithm.

scholarly journals Optimization of a cable support strategy for roadways in coal mines by using the PSO algorithm

2021 ◽  
Author(s):  
Zhu Tang ◽  
Shuqing Li ◽  
Fei Huang ◽  
Junwei Yang ◽  
Fuling Yang ◽  
...  
Keyword(s):  
Numerical Simulation ◽  
Particle Swarm Optimization ◽  
Coal Mines ◽  
Pso Algorithm ◽  
Engineering Practice ◽  
Lateral Stress ◽  
Swarm Optimization ◽  
Support Strategy ◽  
Simulation Results ◽  
Roadway Support

Abstract Cables are commonly used for roadway support in coal mines. Traditionally, support schemes show characteristics of excessive strength and resource waste; therefore, determining how to scientifically and economically arrange the distribution of cables is important for engineering practice. To obtain the best distribution of cables, in this paper, the particle swarm optimization (PSO) algorithm and FLAC3D numerical simulation were combined to conduct numerical simulations. Finally, the best cable distribution considering safety and economy was determined. By analyzing the numerical simulation results, it can be concluded that the PSO algorithm can be applied to determine the optimal cable distribution for roadway support and can be applied to engineering practice. In addition, the best cable arrangement of a roadway under different lateral stress coefficients was obtained, and it can be concluded that the cable arrangement should be adjusted according to specific circumstances.

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