Multi-Objective Optimum Design of a Neuro-Fuzzy Network Using a Combined PSO and DE Algorithm Based on Fuzzy Logic

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

POURHASHEMI H.; JAMALI A.; NARIMANZADEH N.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Modares Mechanical Engineering Year:2019 | Volume:19 | Issue:2 Page(s): 475-482

Download Full-Text

Persian Verion

View:

346

Download:

Cites:

Information Journal Paper

Title

Multi-Objective Optimum Design of a Neuro-Fuzzy Network Using a Combined PSO and DE Algorithm Based on Fuzzy Logic

Author(s)

POURHASHEMI H. | JAMALI A. | NARIMANZADEH N. | Issue Writer Certificate

Keywords

OptimizationQ1

Particle swarm optimizationQ1

Differential evolutionQ1

Fuzzy logicQ1

Neuro-Fuzzy networkQ1

Abstract

Because of the widespread application in complex modeling based on experimental data, Neuro-Fuzzy networks have attracted the attention of researchers. In the neuro-fuzzy inference system, the objective is to reduce the system’ s prediction error relative to the actual data. The regulation of parameters of Neuro-Fuzzy network is very important and affects its performance. So, a new Optimization algorithm based on Particle Swarm Optimization (PSO) and Differential evolution (DE) has been proposed. In this algorithm, the coefficients of the operator speed are calculated dynamically, using Fuzzy logic. These coefficients are set according to the generation number and variance of the particles. Proposed operator leads the particles to explore and exploit the search domain more precisely. Next, the performance of the proposed algorithm is checked by optimizing three benchmarks and comparing it with the results, which are obtained by conventional PSO and DE. The results show that the proposed algorithm obtained better solution in comparison with DE and PSO and proved its performance and efficiency. Finally, a neuro-fuzzy system has been employed to forecast the time series of Mackey-Glass. The parameters of this Neuro-Fuzzy network are optimized by the new algorithm and the PSO and DE method multi-objectively and the Pareto charts obtained by each method of Optimization are compared with each other, indicating the better performance of the new algorithm.

Cites

No record.

References

No record.

Cite

APA: Copy

POURHASHEMI, H., JAMALI, A., & NARIMANZADEH, N.. (2019). Multi-Objective Optimum Design of a Neuro-Fuzzy Network Using a Combined PSO and DE Algorithm Based on Fuzzy Logic. MODARES MECHANICAL ENGINEERING, 19(2 ), 475-482. SID. https://sid.ir/paper/179668/en

Vancouver: Copy

POURHASHEMI H., JAMALI A., NARIMANZADEH N.. Multi-Objective Optimum Design of a Neuro-Fuzzy Network Using a Combined PSO and DE Algorithm Based on Fuzzy Logic. MODARES MECHANICAL ENGINEERING[Internet]. 2019;19(2 ):475-482. Available from: https://sid.ir/paper/179668/en

IEEE: Copy

H. POURHASHEMI, A. JAMALI, and N. NARIMANZADEH, “Multi-Objective Optimum Design of a Neuro-Fuzzy Network Using a Combined PSO and DE Algorithm Based on Fuzzy Logic,” MODARES MECHANICAL ENGINEERING, vol. 19, no. 2 , pp. 475–482, 2019, [Online]. Available: https://sid.ir/paper/179668/en

Related Journal Papers