scholarly journals A Concept of the Non-Stationary Filtering Network with Reduced Transient Response

2019 ◽  
Vol 9 (21) ◽  
pp. 4570
Author(s):  
Katarzyna Wiechetek ◽  
Jacek Piskorowski
Keyword(s):  
Transient Response ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Gain Factor ◽  
Time Varying ◽  
Swarm Optimization ◽  
First Order ◽  
Analog System ◽  
Time Varying Parameters ◽  
Discretization Process

This paper presents a concept of the non-stationary filtering network with reduced transient response consisting of the first-order digital elements with time-varying parameters. The digital filter section is based on the analog system. In order to design the filtering network, the analog prototype was subjected to the discretization process. The time constant and the gain factor were then temporarily varied in time in order to suppress the transient response of the designed filtering structure. The optimization method, based on the Particle Swarm Optimization (PSO) algorithm which is aimed at reducing the settling time by a proper parameter manipulation, is presented. Simulation results proving the usefulness of the proposed concept are also shown and discussed.

scholarly journals Path Planning for Mobile Objects in Four-Dimension Based on Particle Swarm Optimization Method with Penalty Function

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Yong Ma ◽  
M. Zamirian ◽  
Yadong Yang ◽  
Yanmin Xu ◽  
Jing Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Penalty Function ◽  
Particle Swarm ◽  
Goal Function ◽  
Optimization Method ◽  
Mobile Object ◽  
Time Varying ◽  
Swarm Optimization ◽  
Calculus Of Variation ◽  
Mobile Objects

We present one algorithm based on particle swarm optimization (PSO) with penalty function to determine the conflict-free path for mobile objects in four-dimension (three spatial and one-time dimensions) with obstacles. The shortest path of the mobile object is set as goal function, which is constrained by conflict-free criterion, path smoothness, and velocity and acceleration requirements. This problem is formulated as a calculus of variation problem (CVP). With parametrization method, the CVP is converted to a time-varying nonlinear programming problem (TNLPP). Constraints of TNLPP are transformed to general TNLPP without any constraints through penalty functions. Then, by using a little calculations and applying the algorithm PSO, the solution of the CVP is consequently obtained. Approach efficiency is confirmed by numerical examples.

scholarly journals Short Transient Parameter-Varying IIR Filter Based on Analog Oscillatory System

2019 ◽  
Vol 9 (10) ◽  
pp. 2013 ◽  
Author(s):  
Piotr Okoniewski ◽  
Jacek Piskorowski
Keyword(s):  
Transient Response ◽  
Real Life ◽  
Digital Filtering ◽  
Oscillatory System ◽  
Optimization Method ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Dynamical Properties ◽  
Simulation Results ◽  
Discretization Process

This paper presents a concept for digital infinite impulse response (IIR) lowpass filter with reduced transient response. The proposed digital filtering structure is based on an analog oscillatory system. In order to design the considered digital filter, the analog prototype is subjected to a discretization process and, then, the parameters describing the dynamical properties of the oscillatory system are temporarily varied in time, so as to suppress the transient response of the designed filter. An optimization method, aimed at reducing the settling time by proper parameter manipulation, is presented. Simulation results, along with a real-life application proving the usefulness of the proposed concept, are also shown and discussed.

A New Kind of Optimization Method for Gear Design

2012 ◽  
Vol 190-191 ◽  
pp. 7-10 ◽  
Author(s):  
Yu Guo Wu
Keyword(s):  
Simulated Annealing Algorithm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Bp Algorithm ◽  
Mean Square ◽  
Test Results ◽  
Swarm Optimization ◽  
Design Efficiency ◽  
Gear Design ◽  
Annealing Algorithm

In order to raise the design efficiency and get the most excellent design effect, this paper combined Particle Swarm Optimization (PSO) algorithm and put forward a new kind of neural network, based on PSO algorithm and NARMA model. It gives the basic theory, steps and algorithm; The test results show that rapid global convergence and reached the lesser mean square error MSE) when compared with Genetic Algorithm, Simulated Annealing Algorithm, the BP algorithm with momentum term.

A dynamic particle swarm optimization method applied to global optimizations of engineering inverse problem

2018 ◽  
Vol 37 (1) ◽  
pp. 98-117 ◽  
Author(s):  
Shafiullah Khan ◽  
Shiyou Yang ◽  
Obaid Ur Rehman
Keyword(s):  
Inverse Problem ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Content Type ◽  
Novel Strategy ◽  
Modified Pso

Purpose The aim of this paper is to explore the potential of particle swarm optimization (PSO) algorithm to solve an electromagnetic inverse problem. Design/methodology/approach A modified PSO algorithm is designed. Findings The modified PSO algorithm is a more stable, robust and efficient global optimizer for solving the well-known benchmark optimization problems. The new mutation approach preserves the diversity of the population, whereas the proposed dynamic and adaptive parameters maintain a good balance between the exploration and exploitation searches. The numerically experimental results of two case studies demonstrate the merits of the proposed algorithm. Originality/value Some improvements, such as the design of a new global mutation mechanism and introducing a novel strategy for learning and control parameters, are proposed.

Quantitative Feedback Theory control design using particle swarm optimization method

2011 ◽  
Vol 34 (4) ◽  
pp. 463-476 ◽  
Author(s):  
Hazem I Ali ◽  
Samsul Bahari B Mohd Noor ◽  
SM Bashi ◽  
Mohammad Hamiruce Marhaban
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Design Procedure ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Quantitative Feedback Theory ◽  
Swarm Optimization ◽  
Servo Actuator ◽  
Actuator System ◽  
And Performance

In this paper, a particle swarm optimization (PSO) method is proposed to design Quantitative Feedback Theory (QFT) control. This method minimizes a proposed cost function subject to appropriate robust stability and performance QFT constraints. The PSO algorithm is simple and easy to implement, and can be used to automate the loop shaping procedures of the standard QFT. The proposed method is applied to the high uncertainty pneumatic servo actuator system as an example to illustrate the design procedure of the proposed algorithm. The proposed method is compared with the standard QFT control. The results show that the superiority of the proposed method in that it can achieve the same robustness requirements of standard QFT control with simple structure and low order controller.

The Analysis of Particle Swarm Optimization Used for Distribution Network Fault Location

2013 ◽  
Vol 325-326 ◽  
pp. 604-609 ◽  
Author(s):  
Gang Feng ◽  
Jun Zhou ◽  
Dan Li Liu ◽  
Kang Lin Fan ◽  
Zhan Long Zhang ◽  
...  
Keyword(s):  
Fault Location ◽  
Distribution Network ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Basic Principles ◽  
Intelligent Optimization ◽  
Fast Convergence Rate ◽  
Location Algorithm ◽  
Network Fault

In view of their own limitations of GA[1] and ACA[2][3] in the fault location of distribution network, this paper presents a new group intelligent optimization method which is simple and easy to implement, fast convergence rate, more global optimization ability, etc, and this is the PSO. Then the basic principles of the PSO algorithm and the Standard algorithm of PSO are discussed in details, on the base of that, this paper continues to introduce the principle of PSO algorithm which is used in the fault location of distribution network. This section mainly introduces the structure of distribution network, then makes a brief introduction of the fault location algorithm, finally writes out the detailed steps of PSO algorithm. When already understood the principles and algorithms, a detailed analysis of the distribution network. This paper makes use of this method in the process of the location of transformer combined with the high accuracy of fault location which avoid the possibility of local convergence. Finally this paper gives the analysis of case, the result shows that the proposed method of distribution network fault location is feasible.

Application of Particle Swarm Optimization in Cylinder Helical HGearH Multi-Objective Design

2011 ◽  
Vol 474-476 ◽  
pp. 2229-2233
Author(s):  
Yuan Bin Mo ◽  
Ji Zhong Liu ◽  
Bao Lei Wang ◽  
Wei Min Wan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Design ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective

Cylinder helical gGear is widely used in industry. Multi-objective optimization design of the component is often met in its different application sSituation. In this paper a novel multi-objective optimization method based on Particle Swarm Optimization (PSO) algorithm is designed for applying to solve this kind of problem. A paradigm depicted in the paper shows the algorithm is practical.

scholarly journals Signal Recognition Based on APSO-RBF Neural Network to Assist Athlete’s Competitive Ability Evaluation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Feng Guo ◽  
Qingcheng Huang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Particle Swarm Optimization ◽  
Competitive Ability ◽  
Rbf Neural Network ◽  
Pso Algorithm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Technical Ability ◽  
Real Value

The advanced analysis and research methods of big data will provide theoretical support for the integration of athletes’ talent training. The advanced technological methods of big data will also give full play to the advantages of tapping the potential of talents and actively improve the success rate of grassroots young athletes. This paper proposes an improved Adaptive Particle Swarm Optimization (APSO) algorithm for the optimization of radial basis function (RBF) neural network parameters. The basic structure of RBF neural network is introduced, and the influence of parameters on the performance of RBF neural network is analyzed. The optimization method of RBF neural network parameters is analyzed, and Particle Swarm Optimization (PSO) algorithm is selected as the parameter optimization method of RBF neural network. In addition, an improved APSO algorithm is proposed according to the advantages and disadvantages of PSO and compared with other PSO algorithms. Experimental results show that the improved PSO algorithm has better accuracy. The improved PSO algorithm is applied to the parameter optimization of RBF neural network, and the experimental results prove the superiority of the proposed method. By weighting the second-level indicators, the weights of the second-level indicators of athletes’ competitive ability are in order of skill, athletic quality, psychological ability, and artistic expression. Skills are the main factors that determine the level of competitive ability. Sports quality and psychological ability are important guarantees for supporting the normal performance of skills. Artistic expressiveness is a supplementary factor for competitive ability. The various elements cooperate with each other and interact with each other. The indicators do not exist alone but cooperate with each other to support the formation of the entire athletic ability system. In the content of the competitive ability index of excellent athletes, technical ability is the core, and sports quality, psychological ability, and artistic performance ability ultimately exist to serve the improvement of technical ability. The competition scores of the 100 athletes counted in this article are all above 9.0 points. The difference between APSO-RBF’s action quality scores of 100 athletes and the real value is less than 3%. In terms of movement difficulty, the APSO-RBF evaluated athletes’ scores are close to 1.65 points, which is basically the same as the real value.

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