DESIGNING STABLE NEURAL IDENTIFIER BASED ON LYAPUNOV METHOD

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ALIBAKHSHI F.; TESHNEHLAB M.; ALIBAKHSHI M.; MANSOURI M.

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Journal Paper

Paper Information

Journal: JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING Year:2015 | Volume:3 | Issue:2 Page(s): 141-147

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Information Journal Paper

Title

DESIGNING STABLE NEURAL IDENTIFIER BASED ON LYAPUNOV METHOD

Author(s)

ALIBAKHSHI F. | TESHNEHLAB M. | ALIBAKHSHI M. | MANSOURI M. | Issue Writer Certificate

Keywords

GRADIENT DESCENT ALGORITHMQ2

IDENTIFIERQ2

LEARNING RATEQ2

LYAPUNOV STABILITY THEORYQ2

Abstract

The stability of LEARNING RATE in neural network IDENTIFIERs and controllers is one of the challenging issues, which attract many researchers’ interest in neural networks. This paper suggests adaptive GRADIENT DESCENT ALGORITHM with stable learning laws for modified dynamic neural network (MDNN) and studies the stability of this algorithm. Also, stable learning algorithm for parameters of MDNN is proposed. By the proposed method, some constraints are obtained for LEARNING RATE. LYAPUNOV STABILITY THEORY is applied to study the stability of the proposed algorithm. The LYAPUNOV STABILITY THEORY guaranteed the stability of the learning algorithm. In the proposed method, the LEARNING RATE can be calculated online and will provide an adaptive LEARNING RATE for the MDNN structure. Simulation results are given to validate the results.

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APA: Copy

ALIBAKHSHI, F., TESHNEHLAB, M., ALIBAKHSHI, M., & MANSOURI, M.. (2015). DESIGNING STABLE NEURAL IDENTIFIER BASED ON LYAPUNOV METHOD. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, 3(2), 141-147. SID. https://sid.ir/paper/255373/en

Vancouver: Copy

ALIBAKHSHI F., TESHNEHLAB M., ALIBAKHSHI M., MANSOURI M.. DESIGNING STABLE NEURAL IDENTIFIER BASED ON LYAPUNOV METHOD. JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING[Internet]. 2015;3(2):141-147. Available from: https://sid.ir/paper/255373/en

IEEE: Copy

F. ALIBAKHSHI, M. TESHNEHLAB, M. ALIBAKHSHI, and M. MANSOURI, “DESIGNING STABLE NEURAL IDENTIFIER BASED ON LYAPUNOV METHOD,” JOURNAL OF ARTIFICIAL INTELLIGENCE AND DATA MINING, vol. 3, no. 2, pp. 141–147, 2015, [Online]. Available: https://sid.ir/paper/255373/en

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