Parameter Estimation for Time-Varying System Based on Combinatorial PSO
نویسندگان
چکیده
In this paper, a novel Particle Swarm Optimization (PSO) identification algorithm for time-varying systems with a colored noise is presented. Presented criterion function can show not only outside system output error but also inside parameters error in order to explain more difference between actual and estimative system. Identification algorithm may consist of many different PSO algorithms that are named the combinatorial PSO. The estimating and tracking of parameters make use of characteristics of different PSO algorithms. The simulation and result show that the identification algorithm for time-varying systems with noise was indeed more efficient and robust in combinatorial PSO comparing with the original particle swarm optimization.
منابع مشابه
Parameter estimation of bilinear systems based on an adaptive particle swarm optimization
Bilinear models can approximate a large class of nonlinear systems adequately and usually with considerable parsimony in the number of coefficients required. This paper presents the application of Particle Swarm Optimization (PSO) algorithm to solve both offline and online parameter estimation problem for bilinear systems. First, an Adaptive Particle Swarm Optimization (APSO) is proposed to inc...
متن کاملTIME-VARYING FUZZY SETS BASED ON A GAUSSIAN MEMBERSHIP FUNCTIONS FOR DEVELOPING FUZZY CONTROLLER
The paper presents a novel type of fuzzy sets, called time-Varying Fuzzy Sets (VFS). These fuzzy sets are based on the Gaussian membership functions, they are depended on the error and they are characterized by the displacement of the kernels to both right and left side of the universe of discourse, the two extremes kernels of the universe are fixed for all time. In this work we focus only on t...
متن کاملA Combinatorial Algorithm for Fuzzy Parameter Estimation with Application to Uncertain Measurements
This paper presents a new method for regression model prediction in an uncertain environment. In practical engineering problems, in order to develop regression or ANN model for making predictions, the average of set of repeated observed values are introduced to the model as an input variable. Therefore, the estimated response of the process is also the average of a set of output values where th...
متن کاملOnline Monitoring for Industrial Processes Quality Control Using Time Varying Parameter Model
A novel data-driven soft sensor is designed for online product quality prediction and control performance modification in industrial units. A combined approach of time variable parameter (TVP) model, dynamic auto regressive exogenous variable (DARX) algorithm, nonlinear correlation analysis and criterion-based elimination method is introduced in this work. The soft sensor performance validation...
متن کاملMulti-machine power system stabilizer design using improved particle swarm optimization (PSO) with time- varying acceleration coefficients
An efficient and most famous tool to enhance damping of the power system low frequency oscillations is the conventional widely used lead-lag Power System Stabilizer (PSS). To achieve the desired level of robust performance under transient situation, selecting a suitable design method for optimal tuning of PSS parameters is very important in multi-machine power system. Because, it is a multimoda...
متن کامل