Parameters Estimation for the New Four-Parameter Nonlinear Muskingum Model Using the Particle Swarm Optimization

2016 ◽  
Vol 30 (7) ◽  
pp. 2143-2160 ◽  
Author(s):  
A. Moghaddam ◽  
J. Behmanesh ◽  
A. Farsijani
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Nonlinear Muskingum Model

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

scholarly journals An Enhanced Segment Particle Swarm Optimization Algorithm for Kinetic Parameters Estimation of the Main Metabolic Model of Escherichia Coli

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 963
Author(s):  
Mohammed Adam Kunna ◽  
Tuty Asmawaty Abdul Kadir ◽  
Muhammad Akmal Remli ◽  
Noorlin Mohd Ali ◽  
Kohbalan Moorthy ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Kinetic Parameters ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Metabolic Model ◽  
Parameters Estimation ◽  
Swarm Optimization ◽  
Distance Minimization ◽  
Kinetic Parameters Estimation

Building a biologic model that describes the behavior of a cell in biologic systems is aimed at understanding the physiology of the cell, predicting the production of enzymes and metabolites, and providing a suitable data that is valid for bio-products. In addition, building a kinetic model requires the estimation of the kinetic parameters, but kinetic parameters estimation in kinetic modeling is a difficult task due to the nonlinearity of the model. As a result, kinetic parameters are mostly reported or estimated from different laboratories in different conditions and time consumption. Hence, based on the aforementioned problems, the optimization algorithm methods played an important role in addressing these problems. In this study, an Enhanced Segment Particle Swarm Optimization algorithm (ESe-PSO) was proposed for kinetic parameters estimation. This method was proposed to increase the exploration and the exploitation of the Segment Particle Swarm Optimization algorithm (Se-PSO). The main metabolic model of E. coli was used as a benchmark which contained 172 kinetic parameters distributed in five pathways. Seven kinetic parameters were well estimated based on the distance minimization between the simulation and the experimental results. The results revealed that the proposed method had the ability to deal with kinetic parameters estimation in terms of time consumption and distance minimization.

ESTIMATING PARAMETERS OF MUSKINGUM MODEL USING AN ADAPTIVE HYBRID PSO ALGORITHM

2014 ◽  
Vol 28 (01) ◽  
pp. 1459003 ◽  
Author(s):  
AIJIA OUYANG ◽  
ZHUO TANG ◽  
KENLI LI ◽  
AHMED SALLAM ◽  
EDWIN SHA
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Dynamic Models ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Test Method ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Hybrid Particle Swarm Optimization ◽  
Nonlinear Programming Method

In order to accelerate the convergence and improve the calculation accuracy for parameter optimization of the Muskingum model, we propose a novel, adaptive hybrid particle swarm optimization (AHPSO) algorithm. With the decreasing of inertial weight factor proposed, this method can gradually converge to a global optimal with elite individuals obtained by hybrid PSO. In the paper, we analyzed the feasibility and the advantages of the AHPSO algorithm. Then, we verified its efficiency and superiority by application of the Muskingum model. We intensively evaluated the error fitting degree based on the comparison with four known formulas: the test method (TM), the least residual square method (LRSM), the nonlinear programming method (NPM), and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The results show that the AHPSO has a higher precision. In addition, we compared the AHPSO algorithm with the binary-encoded genetic algorithm (BGA), the Gray genetic algorithm (GGA), the Gray-encoded accelerating genetic algorithm (GAGA) and the particle swarm optimization (PSO), and results show that AHPSO has faster convergent speed. Moreover, AHPSO has a competitive advantage compared with the above eight methods in terms of robustness. With the efficiency of this approach it can be extended to estimate parameters of other dynamic models.

Self-Tuning Robust Fuzzy Controller Design Based on Multi-Objective Particle Swarm Optimization Adaptation Mechanism

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Edson B. M. Costa ◽  
Ginalber L. O. Serra
Keyword(s):  
Particle Swarm Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Particle Swarm ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Adaptation Mechanism ◽  
Swarm Optimization ◽  
Adaptive Fuzzy ◽  
Multi Objective

In this paper, an adaptive fuzzy controller design methodology via multi-objective particle swarm optimization (MOPSO) based on robust stability criterion is proposed. The plant to be controlled is modeled from its input–output experimental data considering a Takagi–Sugeno (TS) fuzzy nonlinear autoregressive with exogenous input model, by using the fuzzy C-means clustering algorithm (antecedent parameters estimation) and the weighted recursive least squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital proportional-integral-derivative (PID) controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time-varying delay are presented to illustrate the efficiency and applicability of the proposed methodology.

scholarly journals Feature Selection and Classifier Parameters Estimation for EEG Signals Peak Detection Using Particle Swarm Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Asrul Adam ◽  
Mohd Ibrahim Shapiai ◽  
Mohd Zaidi Mohd Tumari ◽  
Mohd Saberi Mohamad ◽  
Marizan Mubin
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Time Domain Analysis ◽  
Peak Detection ◽  
Parameters Estimation ◽  
Eeg Signals ◽  
Classification Rate ◽  
Swarm Optimization ◽  
Time Frequency

Electroencephalogram (EEG) signal peak detection is widely used in clinical applications. The peak point can be detected using several approaches, including time, frequency, time-frequency, and nonlinear domains depending on various peak features from several models. However, there is no study that provides the importance of every peak feature in contributing to a good and generalized model. In this study, feature selection and classifier parameters estimation based on particle swarm optimization (PSO) are proposed as a framework for peak detection on EEG signals in time domain analysis. Two versions of PSO are used in the study: (1) standard PSO and (2) random asynchronous particle swarm optimization (RA-PSO). The proposed framework tries to find the best combination of all the available features that offers good peak detection and a high classification rate from the results in the conducted experiments. The evaluation results indicate that the accuracy of the peak detection can be improved up to 99.90% and 98.59% for training and testing, respectively, as compared to the framework without feature selection adaptation. Additionally, the proposed framework based on RA-PSO offers a better and reliable classification rate as compared to standard PSO as it produces low variance model.

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