A particle swarm optimization approach to nonlinear rational filter modeling

Lin, Yih-Lon; Chang, Wei‐Der; Hsieh, Jer‐Guang

doi:10.1016/j.eswa.2006.12.004

Cited by 85 publications

(41 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…= a k ) and r, are used to minimize the MSEF. The particle swarm optimization (PSO) algorithm [25][26][27] is an evolutionary optimization method to adjust the desired targets and the parameters, which is given by…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Multiple harmonic-source classification using a Self-Organization Feature Map network with voltage–current wavelet transformation patterns

Huang

Lin

2015

Applied Mathematical Modelling

View full text Add to dashboard Cite

a b s t r a c tThis paper proposes multiple harmonic-source classification using a Self-Organization Feature Map (SOFM) network with voltage (V)-current (I) wavelet transformation patterns. Using the V-I wavelet transformation (WT) patterns, a SOFM network is employed to separate non-harmonic loads from non-linear loads in a distribution system. Morlet wavelet functions are used as feature extractors to extract the features from voltage and current signals. These features are constructed from various V-I WT patterns, and can vary with different dilation and translation parameters. Therefore, the selectable features are relatively broad for real-time applications. Two-dimensional (2-D) patterns appear in different harmonic fluctuations with various harmonic orders, load levels, and power factors. A SOFM network is employed to classify the various V-I WT patterns, including non-linear electronic devices, AC/DC motors, and Electric Arc Furnaces (EAFs). By contrast with the traditional SOFM network and the support vector machine (SVM), the testing results show that the proposed method has a fast learning process, a high accuracy, and an adaptive capability with new add-in training patterns. It can be used as an added tool for power quality (PQ) engineers and can be integrated into monitor instruments.

show abstract

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Multiple harmonic-source classification using a Self-Organization Feature Map network with voltage–current wavelet transformation patterns

Huang

Lin

2015

Applied Mathematical Modelling

View full text Add to dashboard Cite

show abstract

“…In this work, the modified PSO version proposed by Lin et al in [26] has been implemented. Under such approach, the new position p +1 of each particle p is calculated by using the following equations:…”

Section: Particle Swarm Optimization (Pso) Pso Proposed Bymentioning

confidence: 99%

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

Cuevas

Gálvez

Hinojosa

et al. 2014

Journal of Applied Mathematics

View full text Add to dashboard Cite

System identification is a complex optimization problem which has recently attracted the attention in the field of science and engineering. In particular, the use of infinite impulse response (IIR) models for identification is preferred over their equivalent FIR (finite impulse response) models since the former yield more accurate models of physical plants for real world applications. However, IIR structures tend to produce multimodal error surfaces whose cost functions are significantly difficult to minimize. Evolutionary computation techniques (ECT) are used to estimate the solution to complex optimization problems. They are often designed to meet the requirements of particular problems because no single optimization algorithm can solve all problems competitively. Therefore, when new algorithms are proposed, their relative efficacies must be appropriately evaluated. Several comparisons among ECT have been reported in the literature. Nevertheless, they suffer from one limitation: their conclusions are based on the performance of popular evolutionary approaches over a set of synthetic functions with exact solutions and well-known behaviors, without considering the application context or including recent developments. This study presents the comparison of various evolutionary computation optimization techniques applied to IIR model identification. Results over several models are presented and statistically validated.

show abstract

“…In 2008, a PSO algorithm was proposed to solve the parameter estimation problems for non-linear dynamic rational filters (Lin Y.L., et al, 2008). The proposed approach had significantly improved the approximation results compared with GA. On the other side, PSO combined chaos to enhance the searching efficiency had greatly improved the evolutionary abilities (Liu et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

A Particle Swarm Optimization Based Chaotic K-Means Evolutionary Approach

Cheng¹,

Huang²,

Chen³

2010

Proceedings -- The 27th International Symposium on Automation and Robotics in Construction

View full text Add to dashboard Cite

The proposed approach brings up a manner of cognitiveness that inherits a paradigm in particle swarm optimization to implement a chaotic mapping and enhanced by K-means clustering algorithm. In this work, named KCPSO, chaotic mapping with ergodicity, irregularity and the stochastic properties in PSO contributes to global search while K-means with clustering properties in PSO results in rapid convergence.Numerical results indicated that PSO adopting chaotic features can more easily escape from local optima, and meanwhile PSO incorporating with K-means can evidently improve convergence speed. Unpredictability and grouping principle underlying chaotic mapping and K-means methods often imply diversity maintenance and convergence potential within the swarm which inevitably lead to a desirable optimal solution.As proven in the experiments with multidimensional search space and compared with original PSO, the conclusions reported that the proposed KCPSO algorithm could improve the search performance on the benchmark functions significantly, and show the effectiveness of solving optimization problems.

show abstract

A particle swarm optimization approach to nonlinear rational filter modeling

Cited by 85 publications

References 11 publications

Multiple harmonic-source classification using a Self-Organization Feature Map network with voltage–current wavelet transformation patterns

Multiple harmonic-source classification using a Self-Organization Feature Map network with voltage–current wavelet transformation patterns

A Comparison of Evolutionary Computation Techniques for IIR Model Identification

A Particle Swarm Optimization Based Chaotic K-Means Evolutionary Approach

Contact Info

Product

Resources

About