A Modified PSO Algorithm for IIR Digital Filter Modeling

2018 ◽  
Vol 27 (05) ◽  
pp. 1850073 ◽  
Author(s):  
Wei-Der Chang
Keyword(s):  
Digital Filter ◽  
System Modeling ◽  
Pso Algorithm ◽  
Infinite Impulse Response ◽  
Reduced Order ◽  
Modified Particle Swarm Optimization ◽  
Iir Filter ◽  
Error Surface ◽  
Digital Modeling ◽  
System Order

Modeling for an unknown plant is a very important and primary task in the control system engineering. By using this established model, an adequate controller can be further designed to meet certain control specifications. This paper will propose a new modeling method which is based on the infinite impulse response (IIR) digital filter structure. A modified particle swarm optimization (PSO) with multiple subpopulations is utilized to solve for the IIR filter coefficients to model an unknown digital system. The developed PSO algorithm is able to overcome the multimodal error surface problem that often occurs in the system modeling of IIR digital filter. Finally, three different kinds of digital systems are examined to demonstrate the feasibility of the proposed method, including the same system order and reduced order structures. It is concluded from several simulation results that the proposed method has an excellent performance on solving the IIR digital modeling problem.

scholarly journals Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

A numerical and experimental implementation and integration of Steiglitz–McBride algorithm with the frequency domain IIR filtering technique for active vibration control

2016 ◽  
Vol 24 (6) ◽  
pp. 1086-1100
Author(s):  
Utku Boz ◽  
Ipek Basdogan
Keyword(s):  
Computational Complexity ◽  
Vibration Control ◽  
Frequency Domain ◽  
Impulse Response ◽  
Time Domain ◽  
Vibration Suppression ◽  
Active Vibration Control ◽  
Infinite Impulse Response ◽  
Iir Filter ◽  
Active Vibration

In adaptive control applications for noise and vibration, finite ımpulse response (FIR) or ınfinite ımpulse response (IIR) filter structures are used for online adaptation of the controller parameters. IIR filters offer the advantage of representing dynamics of the controller with smaller number of filter parameters than with FIR filters. However, the possibility of instability and convergence to suboptimal solutions are the main drawbacks of such controllers. An IIR filtering-based Steiglitz–McBride (SM) algorithm offers nearly-optimal solutions. However, real-time implementation of the SM algorithm has never been explored and application of the algorithm is limited to numerical studies for active vibration control. Furthermore, the prefiltering procedure of the SM increases the computational complexity of the algorithm in comparison to other IIR filtering-based algorithms. Based on the lack of studies about the SM in the literature, an SM time-domain algorithm for AVC was implemented both numerically and experimentally in this study. A methodology that integrates frequency domain IIR filtering techniques with the classic SM time-domain algorithm is proposed to decrease the computational complexity. Results of the proposed approach are compared with the classical SM algorithm. Both SM and the proposed approach offer multimodal vibration suppression and it is possible to predict the performance of the controller via simulations. The proposed hybrid approach ensures similar vibration suppression performance compared to the classical SM and offers computational advantage as the number of control filter parameters increases.

Information-Applied Technology with Nonlinear System Modeling Method of Elman Network Based on Particle Swarm Optimization

2014 ◽  
Vol 952 ◽  
pp. 307-310
Author(s):  
Yan Ming Wei ◽  
Hua Ping Li ◽  
Hai Long Gao
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear System ◽  
System Modeling ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Modeling Method ◽  
Elman Network ◽  
Swarm Optimization ◽  
Nonlinear System Modeling ◽  
Fast Training

In order to improve the modeling ability for nonlinear system, an Elman modeling method based on Particle Swarm Optimization (PSO) algorithm is proposed. It uses PSO algorithm to optimize the parameters of Elman network. The simulation result shows that the proposed hybrid method combined Elman with PSO algorithm has a good modeling performance with fast training rate for complex nonlinear system.

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.

scholarly journals Structure of All-Digital Frequency Synthesiser for IoT and IoV Applications

Electronics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 29 ◽  
Author(s):  
Marijan Jurgo ◽  
Romualdas Navickas
Keyword(s):  
Phase Noise ◽  
Digital Filter ◽  
High Performance ◽  
Domain Model ◽  
Infinite Impulse Response ◽  
Type I ◽  
Noise Sources ◽  
Digital Frequency ◽  
Digitally Controlled Oscillator ◽  
The Impact

In recent years number of Internet of Things (IoT) services and devices is growing and Internet of Vehicles (IoV) technologies are emerging. Multiband transceiver with high performance frequency synthesisers should be used to support a multitude of existing and developing wireless standards. In this paper noise sources of an all-digital frequency synthesiser are discussed through s-domain model of frequency synthesisers, and the impact of noise induced by main blocks of synthesisers to the overall phase noise of frequency synthesisers is analysed. Requirements for time to digital converter (TDC), digitally controlled oscillator (DCO) and digital filter suitable for all-digital frequency synthesiser for IoT and IoV applications are defined. The structure of frequency synthesisers, which allows us to meet defined requirements, is presented. Its main parts are 2D Vernier TDC based on gated ring oscillators, which can achieve resolution close to 1 ps; multi core LC-tank DCO, whose tuning range is 4.3–5.4 GHz when two cores are used and phase noise is −116.4 dBc/Hz at 1 MHz offset from 5.44 GHz carrier; digital filter made of proportional and integral gain stages and additional infinite impulse response filter stages. Such a structure allows us to achieve a synthesiser’s in-band phase noise lower than −100 dBc/Hz, out-of-band phase noise equal to −134.0 dBc/Hz and allows us to set a synthesiser to type-I or type-II and change its order from first to sixth.

Design of Neural Network for Satellite’s Attitude Control Systems

2011 ◽  
Vol 110-116 ◽  
pp. 5001-5008
Author(s):  
Salomon Montenegro ◽  
Lenin Luna
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Activation Function ◽  
Infinite Impulse Response ◽  
Attitude Control System ◽  
Dynamic Neural Network ◽  
Iir Filter ◽  
Communication Satellite ◽  
Satellite Ground Station ◽  
Residual Generator

The objective of this paper is to design a neural network-based residual generator to detect the fault in the actuators for a specific communication satellite in its attitude control system (ACS). First, a design of dynamic neural network with dynamic neurons is done, those neurons corresponds a second order linear Infinite Impulse Response (IIR) filter and a nonlinear activation function with adjustable parameters. Second, the parameters from the network are adjusted to minimize a performance index specified by the output estimated error, with the given input-output data collected from the specific ACS. Then, the designed dynamic neural network is trained and applied for detecting the faults injected to the wheel, which is the main actuator in the normal mode for the communication satellite. Then the performance and capabilities of the designed network were tested and compared with a conventional model-based observer residual, showing the differences between these two methods, and indicating the benefit of the proposed design to know the real status of the momentum wheel. Finally, the application of the methods in a satellite ground station is discussed.

Efficient Multirate Filtering

2002 ◽  
pp. 105-142 ◽  
Author(s):  
Ljiljana D. Milic ◽  
Miroslav D. Lutovac
Keyword(s):  
Digital Filter ◽  
Filter Design ◽  
Narrow Band ◽  
Sampling Rate ◽  
Wide Band ◽  
Digital Filter Design ◽  
Iir Filter ◽  
Design And Implementation ◽  
Sampling Rate Conversion ◽  
Rate Conversion

Application of multirate techniques to improve digital filter design and implementation are considered in this chapter. FIR and IIR filter design and implementation for sampling rate conversion by integer and rational factors are presented. Sharp narrow-band and wide-band multirate design techniques are discussed. Accurate designs of FIR and IIR half-band filters are described in detail. Several examples are provided to illustrate the multirate approach to filter design.

Optimal design of photovoltaic solar systems considering shading effect and hourly radiation using a modified PSO algorithm

SIMULATION ◽  
2019 ◽  
Vol 95 (10) ◽  
pp. 931-939 ◽  
Author(s):  
Mohammad Hossein Shams ◽  
Mohsen Kia ◽  
Alireza Heidari ◽  
Daming Zhang
Keyword(s):  
Energy Demand ◽  
Pso Algorithm ◽  
Minimum Area ◽  
Solar Systems ◽  
Modified Particle Swarm Optimization ◽  
Pv Systems ◽  
Roof Area ◽  
Solar Field ◽  
The Given

Regarding the significant potential of solar energy in Iran, implementation of optimally designed photovoltaic (PV) systems can be effective. Hence, this study proposes two objective functions: first, the maximum possible output energy for a given area and, second, the minimum area receiving a given yearly energy from PV fixed collectors in a solar field, both of which are calculated. In addition, the shading and masking effects are considered in the calculations. A modified particle swarm optimization (MPSO) algorithm is used to solve the optimization problem. The case study of this article is a shopping center in Isfahan-Iran (latitude 32.5°N) with the minimum yearly energy demand of 171 MWh and the 5000 m2 roof area. To evaluate the yearly energy, the calculated hourly radiation approach is applied to the case study. The results show that the maximum possible generated energy is 881 MWh/year for the given area. In addition, to provide the minimum demand, 720 m2 area of roof is needed. To verify the effectiveness of the proposed MPSO, the results are compared with those of obtained by the relevant commercial software.

scholarly journals Comparison of Circular Symmetric Low-Pass Digital IIR Filter Design Using Evolutionary Computation Techniques

Mathematics ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 1226 ◽  
Author(s):  
Omar Avalos ◽  
Erik Cuevas ◽  
Jorge Gálvez ◽  
Essam H. Houssein ◽  
Kashif Hussain
Keyword(s):  
Evolutionary Computation ◽  
Transfer Functions ◽  
Filter Design ◽  
Statistical Treatment ◽  
Optimization Methods ◽  
Intelligence Community ◽  
Infinite Impulse Response ◽  
Iir Filters ◽  
Iir Filter ◽  
Wide Range

The design of two-dimensional Infinite Impulse Response (2D-IIR) filters has recently attracted attention in several areas of engineering because of their wide range of applications. Synthesizing a user-defined filter in a 2D-IIR structure can be interpreted as an optimization problem. However, since 2D-IIR filters can easily produce unstable transfer functions, they tend to compose multimodal error surfaces, which are computationally difficult to optimize. On the other hand, Evolutionary Computation (EC) algorithms are well-known global optimization methods with the capacity to explore complex search spaces for a suitable solution. Every EC technique holds distinctive attributes to properly satisfy particular requirements of specific problems. Hence, a particular EC algorithm is not able to solve all problems adequately. To determine the advantages and flaws of EC techniques, their correct evaluation is a critical task in the computational intelligence community. Furthermore, EC algorithms are stochastic processes with random operations. Under such conditions, for obtaining significant conclusions, appropriate statistical methods must be considered. Although several comparisons among EC methods have been reported in the literature, their conclusions are based on a set of synthetic functions, without considering the context of the problem or appropriate statistical treatment. This paper presents a comparative study of various EC techniques currently in use employed for designing 2D-IIR digital filters. The results of several experiments are presented and statistically analyzed.

Monopole-Gear Design Based on Neural Network and Modified Particle Swarm Optimization

2013 ◽  
Vol 477-478 ◽  
pp. 368-373 ◽  
Author(s):  
Hai Rong Fang
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Simulated Annealing Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Results ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
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, which based on PSO algorithm, and the implementing framework of PSO 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.

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