scholarly journals A Parameter Estimation Method for Nonlinear Systems Based on Improved Boundary Chicken Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Shaolong Chen ◽  
Renyu Yang ◽  
Renhuan Yang ◽  
Liu Yang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
System Modeling ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Function Parameter ◽  
Swarm Optimization ◽  
Modeling And Control ◽  
Chicken Swarm Optimization

Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO) has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO) is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.

scholarly journals Nonintrusive Method for Induction Motor Equivalent Circuit Parameter Estimation using Chicken Swarm Optimization (CSO) Algorithm

2021 ◽  
Vol 18 (1) ◽  
pp. 21-29
Author(s):  
M. Aminu ◽  
M. Abana ◽  
S.W. Pallam ◽  
P.K. Ainah
Keyword(s):  
Parameter Estimation ◽  
Equivalent Circuit ◽  
Induction Motor ◽  
Estimation Method ◽  
Equivalent Circuit Model ◽  
Test Method ◽  
Equivalent Circuits ◽  
Circuit Parameter ◽  
Swarm Optimization ◽  
Chicken Swarm Optimization

This paper presents a nonintrusive method for estimating the parameters of an Induction Motor (IM) without the need for the conventional no-load and locked rotor tests. The method is based on a relatively new swarm-based algorithm called the Chicken Swarm Optimization (CSO). Two different equivalent circuits implementations have been considered for the parameter estimation scheme (one with parallel and the other with series magnetization circuit). The proposed parameter estimation method was validated experimentally on a standard 7.5 kW induction motor and the results were compared to those obtained using the IEEE Std. 112 reduced voltage impedance test method 3. The proposed CSO optimization method gave accurate estimates of the IM equivalent circuit parameters with maximum absolute errors of 5.4618% and 0.9285% for the parallel and series equivalent circuits representations respectively when compared to the IEEE Std. 112 results. However, standard deviation results in terms of the magnetization branch parameters, suggest that the series equivalent circuit model gives more repeatable results when compared to the parallel equivalent circuit. Keywords: Induction motor, Chicken Swarm Optimization, parameter estimation, equivalent circuit, objective function

An Optimal Way of VM Placement Strategy in Cloud Computing Platform Using ABCS Algorithm

2021 ◽  
Vol 12 (3) ◽  
pp. 16-38
Author(s):  
Pushpa R. ◽  
M. Siddappa
Keyword(s):  
Cloud Computing ◽  
Power Consumption ◽  
Virtual Machines ◽  
Fitness Function ◽  
Vm Placement ◽  
Migration Strategy ◽  
Swarm Optimization ◽  
Bee Colony ◽  
Migration Cost ◽  
Chicken Swarm Optimization

In this paper, VM replacement strategy is developed using the optimization algorithm, namely artificial bee chicken swarm optimization (ABCSO), in cloud computing model. The ABCSO algorithm is the integration of the artificial bee colony (ABC) in chicken swarm optimization (CSO). This method employed VM placement based on the requirement of the VM for the completion of the particular task using the service provider. Initially, the cloud system is designed, and the proposed ABCSO-based VM placement approach is employed for handling the factors, such as load, CPU usage, memory, and power by moving the virtual machines optimally. The best VM migration strategy is determined using the fitness function by considering the factors, like migration cost, load, and power consumption. The proposed ABCSO method achieved a minimal load of 0.1688, minimal power consumption of 0.0419, and minimal migration cost of 0.0567, respectively.

A parameter estimation method for fractional-order nonlinear systems based on improved whale optimization algorithm

2019 ◽  
Vol 33 (07) ◽  
pp. 1950075 ◽  
Author(s):  
Gong Ren ◽  
Renhuan Yang ◽  
Renyu Yang ◽  
Pei Zhang ◽  
Xiuzeng Yang ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Nonlinear Systems ◽  
Fractional Order ◽  
Optimization Algorithm ◽  
Fitness Function ◽  
Whale Optimization Algorithm ◽  
Engineering System ◽  
Grasshopper Optimization Algorithm ◽  
Whale Optimization ◽  
System Characteristics

Compared to the integer-order systems, the system characteristics of the fractional system are closer to the system characteristics of the real engineering system, the study found beyond that, strictly speaking, various physical phenomena in nature are nonlinear. The problem of parameter estimation problem of fractional-order nonlinear systems can be transformed into the problem of parameter optimization problem by constructing an appropriate fitness function. This paper proposes a hybrid improvement algorithm based on whale optimization algorithm (WOA) to solve this problem and verify it both in Lorenz system and Lu system. The simulation result shows that the hybrid improved algorithm is superior to genetic algorithm (GA), particle swarm optimization (PSO), grasshopper optimization algorithm (GOA) and WOA in convergence speed and accuracy.

Feature Selection in Evolutionary Algorithm-Based Parameter Estimation of Duffing Oscillators

2013 ◽  
Author(s):  
Amit Banerjee ◽  
Issam Abu Mahfouz ◽  
Ma’moun Abu-Ayyad
Keyword(s):  
Parameter Estimation ◽  
Differential Evolution ◽  
Fitness Function ◽  
Optimal Solution ◽  
Optimization Techniques ◽  
Transient Effects ◽  
Finite Sample ◽  
Statistical Parameters ◽  
Swarm Optimization ◽  
Evolutionary Optimization Techniques

The use of evolutionary optimization techniques such as genetic algorithms, differential evolution, swarm optimization and genetic programming to solve the inverse problem of parameter estimation for nonlinear chaotic systems has been gaining popularity in recent years. The efficacy of such evolutionary schemes depends on the definition of a suitable fitness function which is used to compare potential solutions in the population. In almost all research involving evolutionary schemes for parameter identification, displacement values of the first few hundred Poincaré points, after ignoring transient effects, have been used as the feature set. The measured response of the system is compared to the response of the potential solutions in the population over these Poincaré points, although there is no empirical research to show that such a feature set works better than other possible feature sets. In this paper, a smaller feature set based on first and second-order statistical parameters of the response are considered and the estimation results are compared to the estimate produced by using the standard Poincaré points-based feature set, called the finite sample feature set in this paper. Also compared are results using three evolutionary algorithms — firefly algorithm, particle swarm optimization and differential evolution. It has been shown that the proposed feature set converges to a near-optimal solution faster and in fewer generations and produces estimates that are comparable to those obtained with the finite sample feature set.

scholarly journals Joint Angle-Amplitude Estimation for Multiple Signals with L-Structured Arrays Using Bioinspired Computing

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Fawad Zaman
Keyword(s):  
Proximity Effect ◽  
Joint Angle ◽  
Fitness Function ◽  
Pattern Search ◽  
Estimation Accuracy ◽  
Unknown Parameters ◽  
Swarm Optimization ◽  
Multiple Signals ◽  
Amplitude Estimation ◽  
Bioinspired Computing

The aim of this work is to estimate jointly the elevation and azimuth angles along with the amplitudes of multiple signals impinging on 1-L- and 2-L-shape arrays. An efficient mechanism based on hybrid Bioinspired techniques is proposed for this purpose. The global search optimizers such as Differential Evolution (DE) and Particle Swarm optimization (PSO) are hybridized with a local search optimizer called pattern search (PS). Approximation theory in Mean Square Error sense is exploited to develop a fitness function of the problem. The unknown parameters of multiple signals transmitted by far-field sources are estimated with the strength of hybrid DE-PS and PSO-PS. The effectiveness of the proposed techniques is tested in terms of estimation accuracy, proximity effect, convergence, and computational complexity.

scholarly journals SIS-BAM Algorithm for Angular Parameter Estimation of 2-D Incoherently Distributed Sources

2021 ◽  
Author(s):  
Fulai Liu ◽  
Kai Tang ◽  
Hao Qin
Keyword(s):  
Parameter Estimation ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Angular Parameter ◽  
Invariant Structure ◽  
Closed Form Solutions ◽  
Low Dimension ◽  
Incoherently Distributed Sources ◽  
Shift Invariance

Abstract For two-dimensional (2-D) incoherently distributed sources, this paper presents an effective angular parameter estimation method based on shift invariant structure (SIS) of the beamspace array manifold (BAM), named as SIS-BAM algorithm. In the proposed method, a shift invariance structure (SIS) of the observed vectors is firstly established utilizing a generalized array manifold of an uniform linear orthogonal array (ULOA). Secondly, based on Fourier basis vectors and the SIS, a beamspace transformation matrix can be performed. It projects received signals into the corresponding beamspace, so as to carry out dimension reduction of observed signals in beamspace domain. Finally, according to the SIS of beamspace observed vectors, the closed form solutions of the nominal azimuth and elevation are derived. Compared with the previous works, the presented SIS-BAM method provides better estimation performance, for example: 1) the computational complexity is reduced due to dealing with low-dimension beamspace signals and avoiding spectral search; 2) it can not only improve the angular parameter estimation accuracy but also have excellent robustness to the change of signal-to-noise ratio (SNR) and snapshot number. The theoretical analysis and simulation results confirm the effectiveness of the proposed method.

scholarly journals Consensus-Based Distributed Target Tracking with False Data Injection Attacks over Radar Network

2021 ◽  
Vol 11 (10) ◽  
pp. 4564
Author(s):  
Yongtao Shui ◽  
Yu Wang ◽  
Yu Li ◽  
Yongzhi Shan ◽  
Naigang Cui ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
State Estimation ◽  
Target Tracking ◽  
Estimation Error ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Consensus Method ◽  
False Data Injection ◽  
Radar Network ◽  
Global Consistency

For target tracking in radar network, any anomaly in a part of the system can quickly spread over the network and lead to tracking failures. False data injection (FDI) attacks can damage the state estimation mechanism by modifying the radar measurements with unknown and time-varying attack variables, therefore making traditional filters inapplicable. To tackle this problem, we propose a novel consensus-based distributed state estimation (DSE) method for target tracking with FDI attacks, which is effective even when all radars are under FDI attacks. First, a real-time residual-based detector is introduced to the DSE framework, which can effectively detect FDI attacks by analyzing the statistical properties of the residual. Secondly, a simple yet effective attack parameter estimation method is proposed to provide attack parameter estimation based on a pseudo-measurement equation, which has the advantage of decoupled estimation of state and attack parameters compared with augmented state filters. Finally, for timely attack mitigation and global consistency achievement, a novel hybrid consensus method is proposed which can compensate for the estimation error caused by FDI attacks and provide estimation accuracy improvement. The simulation results show that the proposed solution is effective and superior to the traditional DSE method for target tracking in the presence of FDI attacks.

PARAMETER ESTIMATION OF SCHOTTKY-BARRIER DIODE MODEL BY PARTICLE SWARM OPTIMIZATION

2009 ◽  
Vol 20 (05) ◽  
pp. 687-699 ◽  
Author(s):  
KAIER WANG ◽  
MEIYING YE
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Schottky Barrier ◽  
Particle Swarm ◽  
Schottky Barrier Diode ◽  
Estimation Accuracy ◽  
Parameter Estimation Problem ◽  
Swarm Optimization ◽  
Computation Efficiency ◽  
Parameter Values

This paper presents particle swarm optimization (PSO) method to solve the parameter estimation problem of the Schottky-barrier diode model. Based on the synthetic and experimental data, we have demonstrated that the proposed method has high parameter estimation accuracy. Besides, the initial guesses for the model parameter values are not required in the PSO method. Also, the performance of the PSO method is compared with that of the genetic algorithm (GA) method. The results indicate that the PSO method outperforms the binary-coded and real-coded GA methods in terms of estimation accuracy and computation efficiency.

scholarly journals Improved Chicken Swarm Algorithms Based on Chaos Theory and Its Application in Wind Power Interval Prediction

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Bing Wang ◽  
Wei Li ◽  
Xianhui Chen ◽  
Haohao Chen
Keyword(s):  
Cost Function ◽  
Wind Power ◽  
Chaos Theory ◽  
Prediction Interval ◽  
Fitness Function ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Interval Prediction ◽  
Swarm Algorithms ◽  
Chicken Swarm Optimization

Probabilistic interval prediction can be used to quantitatively analyse the uncertainty of wind energy. In this paper, a wind power interval prediction model based on chaotic chicken swarm optimization and extreme learning machine (CCSO-ELM) is proposed. Traditional optimization has limitations of low population diversity and a tendency to easily fall into local minima. To address these limitations, chaos theory is adopted in the chicken swarm optimization (CSO), which improves its performance and efficiency. In addition, the traditional cost function does not reflect the deviation degree of off-interval points; hence, an evaluation index considering the relative deviation of off-interval points is proposed in this paper. Finally, the new cost function is taken as the fitness function, the output layer weight of ELM is optimized using CCSO, and the lower upper bound estimation (LUBE) is adopted to output the prediction interval directly. The simulation result shows that the proposed method can effectively reduce the average bandwidth, improve the quality of interval prediction, and guarantee the interval coverage.

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