A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers

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.

AHMADI G.; TESHNEHLAB M.; SOLTANIAN F.

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

Journal Paper

Paper Information

Journal: CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS Year:2018 | Volume:3 | Issue:1 Page(s): 87-108

Download Full-Text

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

Persian Verion

View:

302

Download:

Cites:

Information Journal Paper

Title

A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers

Author(s)

AHMADI G. | TESHNEHLAB M. | SOLTANIAN F. | Issue Writer Certificate

Keywords

Rough-neural network

System identification

Emotional learning

Lyapunov stability theory

Abstract

To enhance the performances of Rough-neural networks (R-NNs) in the System identification, on the base of Emotional learning, a new stable learning algorithm is developed for them. This algorithm facilitates the error convergence by increasing the memory depth of R-NNs. To this end, an emotional signal as a linear combination of identification error and its differences is used to achieve the learning laws. In addition, the error convergence and the boundedness of predictions and parameters of the model are proved. To illustrate the efficiency of proposed algorithm, some nonlinear systems including the cement rotary kiln are identified using this method and the results are compared with some other models.

Cites

No record.

References

No record.

Cite

APA: Copy

AHMADI, G., TESHNEHLAB, M., & SOLTANIAN, F.. (2018). A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers. CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS, 3(1 ), 87-108. SID. https://sid.ir/paper/361396/en

Vancouver: Copy

AHMADI G., TESHNEHLAB M., SOLTANIAN F.. A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers. CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS[Internet]. 2018;3(1 ):87-108. Available from: https://sid.ir/paper/361396/en

IEEE: Copy

G. AHMADI, M. TESHNEHLAB, and F. SOLTANIAN, “A Higher Order Online Lyapunov-Based Emotional Learning for Rough-Neural Identifiers,” CONTROL AND OPTIMIZATION IN APPLIED MATHEMATICS, vol. 3, no. 1 , pp. 87–108, 2018, [Online]. Available: https://sid.ir/paper/361396/en

Related Journal Papers