PROPOSING INTERVAL ACTIVATION FUNCTIONS IN RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT NONLINEAR DYNAMIC SYSTEMS

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.

ZOHOORI ZANGENEH ALLAHYAR; TESHNEHLAB MOHAMMAD; AHMADIEH KHANESAR MOJTABA

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

Journal Paper

Paper Information

Journal: Journal of Control Year:2016 | Volume:9 | Issue:4 Page(s): 1-25

Download Full-Text

Persian Verion

View:

1,614

Download:

Cites:

Information Journal Paper

Title

PROPOSING INTERVAL ACTIVATION FUNCTIONS IN RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT NONLINEAR DYNAMIC SYSTEMS

Author(s)

ZOHOORI ZANGENEH ALLAHYAR | TESHNEHLAB MOHAMMAD | AHMADIEH KHANESAR MOJTABA | Issue Writer Certificate

Keywords

GRANULAR RADIAL BASIS FUNCTION NEURAL NETWORK (GRBFNN)Q2

GRANULAR ACTIVATION FUNCTIONQ2

NOISY DATAQ2

INTERVAL STANDARD DEVIATIONQ2

NONLINEAR DYNAMIC SYSTEMS AND CHAOTIC MODELSQ2

Abstract

A Radial Basis Function Neural Network (RBFNN) is a general approximator. In this paper a GRANULAR ACTIVATION FUNCTION is proposed to improve its learning under the noisy conditions.The GRANULAR ACTIVATION FUNCTION is also named the interval activation function and it is typicaly the Gaussian function which benefits from having a fixed mean and an uncertain standard deviation.The hidden layer of the proposed network has a total of three parameters to train that it consists the means, the lower bounds of the standard deviations and the higher bounds of the standard deviations of the Gaussian functions. The output layer parameters for training are the means of the interval weights and the intervals of the weights.“K-Means clustering algorithm” method is used to trainthese parameters. The purpose of the above learning method is regarded as one of the granular method presenting the bottom-up granulation which causes the input vectors clustered in the larger granules in the hidden layer. Gradient descend method is also used to train these parameters to compare with this novel method. The structure is tested with or without NOISY DATA to identify a nonlinear dynamic system with multiple time delays and to predict a chaotic model, Mackey-Glass.It has been shown that the sensibility related to input alterations reduces because of using the GRANULAR ACTIVATION FUNCTION in RBF Neural Network structure and the response of Granular RBF Neural Network with NOISY DATA is better than RBF Neural Network.

Cites

No record.

References

No record.

Cite

APA: Copy

ZOHOORI ZANGENEH, ALLAHYAR, TESHNEHLAB, MOHAMMAD, & AHMADIEH KHANESAR, MOJTABA. (2016). PROPOSING INTERVAL ACTIVATION FUNCTIONS IN RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT NONLINEAR DYNAMIC SYSTEMS. JOURNAL OF CONTROL, 9(4), 1-25. SID. https://sid.ir/paper/120905/en

Vancouver: Copy

ZOHOORI ZANGENEH ALLAHYAR, TESHNEHLAB MOHAMMAD, AHMADIEH KHANESAR MOJTABA. PROPOSING INTERVAL ACTIVATION FUNCTIONS IN RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT NONLINEAR DYNAMIC SYSTEMS. JOURNAL OF CONTROL[Internet]. 2016;9(4):1-25. Available from: https://sid.ir/paper/120905/en

IEEE: Copy

ALLAHYAR ZOHOORI ZANGENEH, MOHAMMAD TESHNEHLAB, and MOJTABA AHMADIEH KHANESAR, “PROPOSING INTERVAL ACTIVATION FUNCTIONS IN RADIAL BASIS FUNCTION NEURAL NETWORK TO PREDICT NONLINEAR DYNAMIC SYSTEMS,” JOURNAL OF CONTROL, vol. 9, no. 4, pp. 1–25, 2016, [Online]. Available: https://sid.ir/paper/120905/en

Related Journal Papers