Model-free adaptive control optimization using a chaotic particle swarm approach

Coelho, Leandro dos Santos; Coelho, Antônio Augusto Rodrigues

doi:10.1016/j.chaos.2008.08.004

Cited by 81 publications

(17 citation statements)

References 34 publications

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“…Due to the non-repetition of chaos, it can carry out overall exploration at higher velocities than stochastic and ergodic search that depend on probabilities [35].…”

Section: Chaotic Binary Psomentioning

confidence: 99%

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Wang¹,

Feng²,

Liu³

2012

PIER

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Abstract-Traditional optimization methods are not well suitable for thinning large arrays to obtain a low sidelobe level (SLL). The chaotic binary particle swarm optimization (CBPSO) algorithm is presented as a useful alternative for the synthesis of thinned arrays. The proposed algorithm can be improved by nonlinear inertia weight with chaotic mutation to increase the diversity of particles. Two examples have been presented and solved. Simulation results are proposed to compare with published results to verify the effectiveness of the proposed method for both linear and planar arrays.

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“…Due to the non-repetition of chaos, it can carry out overall exploration at higher velocities than stochastic and ergodic search that depend on probabilities [35].…”

Section: Chaotic Binary Psomentioning

confidence: 99%

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Wang¹,

Feng²,

Liu³

2012

PIER

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“…The RNG cannot ensure the optimization’s ergodicity in solution space because they are pseudo-random; therefore, we employed the Rossler chaotic operator [28] to generate parameters ( r 1 , r 2 ). The Rossler equations are as follows:

{\begin{matrix} \frac{d x}{d t} = - false(y + z false) \\ \frac{d y}{d t} = x + a y \\ \frac{d z}{d t} = b + x z - c z \end{matrix}

…”

Section: Forward Neural Networkmentioning

confidence: 99%

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Zhang

2011

Sensors

103

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This paper proposes a hybrid crop classifier for polarimetric synthetic aperture radar (SAR) images. The feature sets consisted of span image, the H/A/α decomposition, and the gray-level co-occurrence matrix (GLCM) based texture features. Then, the features were reduced by principle component analysis (PCA). Finally, a two-hidden-layer forward neural network (NN) was constructed and trained by adaptive chaotic particle swarm optimization (ACPSO). K-fold cross validation was employed to enhance generation. The experimental results on Flevoland sites demonstrate the superiority of ACPSO to back-propagation (BP), adaptive BP (ABP), momentum BP (MBP), Particle Swarm Optimization (PSO), and Resilient back-propagation (RPROP) methods. Moreover, the computation time for each pixel is only 1.08 × 10−7 s.

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“…Despite the fact that PSO is generally used in off-line optimization processes, there are several cases of their application on-line. These applications range from the cancellation of harmonic frequencies, training neural networks and signal estimation, among others, to the same parametric identification [4] and tuning of controllers [9], these last two being directly related to the main idea of this paper. However, due to the requirements for stability of complete system incorporating PSO as an on-line estimator, it is important to pay attention to its stability and convergence properties.…”

Section: Discrete-time Adaptive Systemsmentioning

confidence: 99%

Design of adaptive laws for discrete-time systems based on Particle Swarm Optimization

Ordóñez-Hurtado

Duarte‐Mermoud

2011

2011 9th IEEE International Conference on Control and Automation (ICCA)

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A wide variety of linear/nonlinear adaptive systems in continuous/discrete time can be represented by error models, thus facilitating their analysis. The solution obtained for a given error model can be applied to different systems represented by the error model. This paper presents a methodology for adjusting the parameters of a discrete-time adaptive system represented by a Type 1 Error Model, which is based on Particle Swarm Optimization (PSO) and that allows using it in on-line applications. The performance of the proposed methodology is compared with the traditional gradient and least squares methods through simulations.

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Model-free adaptive control optimization using a chaotic particle swarm approach

Cited by 81 publications

References 34 publications

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Synthesis of Thinned Linear and Planar Antenna Arrays Using Binary Pso Algorithm

Crop Classification by Forward Neural Network with Adaptive Chaotic Particle Swarm Optimization

Design of adaptive laws for discrete-time systems based on Particle Swarm Optimization

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