<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.</p>
</abstract>

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and the least-squares error criterion, a general framework for designing optimal analog wavelet bases is established, which is one of difficult nonlinear constrained optimization problems. Then, to solve this problem, a hybrid algorithm by combining chaotic map particle swarm optimization (CPSO) with local sequential quadratic programming (SQP) is proposed. CPSO is an improved PSO in which the saw tooth chaotic map is used to raise its global search ability. CPSO is a global optimizer to search the estimates of the global solution, while the SQP is employed for the local search and refining the estimates. Benefiting from good global search ability of CPSO and powerful local search ability of SQP, a high-precision global optimum in this problem can be gained. Finally, a series of optimal analog wavelet bases are constructed using the hybrid algorithm. The proposed method is tested for various wavelet bases and the improved performance is compared with previous works.

Optimal Analog Wavelet Bases Construction Using Hybrid Optimization Algorithm

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Optimal Analog Wavelet Bases Construction Using Hybrid Optimization Algorithm"}

An approach for the construction of optimal analog wavelet bases is presented. First, the definition of an analog wavelet is given. Based on the definition and...

Optimal Analog Wavelet Bases Construction Using Hybrid Optimization Algorithm

Data publikacji: 01 gru 2016

Zakres stron: 1918 - 1942

Otrzymano: 29 kwi 2016

Przyjęty: 17 paź 2016

DOI: https://doi.org/10.21307/ijssis-2017-946

Słowa kluczowe
Wavelet transform, Analog wavelet base, Optimization methods, Particle swarm optimization (PSO), Saw tooth chaotic map, Sequential quadratic programming (SQP)

© 2016 Hongmin Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Optimal Analog Wavelet Bases Construction Using Hybrid Optimization Algorithm

Data publikacji: 01 gru 2016

Zakres stron: 1918 - 1942

Otrzymano: 29 kwi 2016

Przyjęty: 17 paź 2016

DOI: https://doi.org/10.21307/ijssis-2017-946

Słowa kluczoweWavelet transform, Analog wavelet base, Optimization methods, Particle swarm optimization (PSO), Saw tooth chaotic map, Sequential quadratic programming (SQP)

© 2016 Hongmin Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Wavelet transform, Analog wavelet base, Optimization methods, Particle swarm optimization (PSO), Saw tooth chaotic map, Sequential quadratic programming (SQP)