NONLINEAR ADAPTIVE NEURAL IDENTIFIER FILTER WITH OPTIMAL LEARNING RATE FOR CONVERGING OF PARAMETERS BASED ON GRADIENT DESCENT

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ALIBAKHSHI FATEMEH; TESHNEHLAB MOHAMMAD; ALIBAKHSHI MEHDI; MANSOURI MOHAMMAD

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Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2015 | Volume:6 | Issue:2 Page(s): 75-86

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Information Journal Paper

Title

NONLINEAR ADAPTIVE NEURAL IDENTIFIER FILTER WITH OPTIMAL LEARNING RATE FOR CONVERGING OF PARAMETERS BASED ON GRADIENT DESCENT

Author(s)

ALIBAKHSHI FATEMEH | TESHNEHLAB MOHAMMAD | ALIBAKHSHI MEHDI | MANSOURI MOHAMMAD | Issue Writer Certificate

Keywords

CONVERGENCEQ2

GARDIENT DESCENT ALGORITHMQ2

NEURAL NETWORKQ2

ONLINEQ2

TAYLOR SERIES EXPANSIONQ2

Abstract

The CONVERGENCE of learning rate in NEURAL NETWORKs identifier and controller is one of challenging issues which attracts great interest from researchers. This paper suggests the adaptive gradient descent algorithm with learning laws which assures the CONVERGENCE of multi-layer perceptron NEURAL NETWORK based on TAYLOR SERIES EXPANSION of output error. In the proposed method the learning rate can be calculated ONLINE. To increase the accuracy and the speed of CONVERGENCE, the second and higher order terms of the TAYLOR SERIES EXPANSION are not considered constant and are updated during the algorithm. Simulating the suggested algorithm on two examples reveals that with considering the bounds in the proposed method, the aims for learning rate, CONVERGENCE of learning algorithm are guaranteed and the speed of CONVERGENCE of training algorithm is increased.

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APA: Copy

ALIBAKHSHI, FATEMEH, TESHNEHLAB, MOHAMMAD, ALIBAKHSHI, MEHDI, & MANSOURI, MOHAMMAD. (2015). NONLINEAR ADAPTIVE NEURAL IDENTIFIER FILTER WITH OPTIMAL LEARNING RATE FOR CONVERGING OF PARAMETERS BASED ON GRADIENT DESCENT. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 6(2), 75-86. SID. https://sid.ir/paper/203010/en

Vancouver: Copy

ALIBAKHSHI FATEMEH, TESHNEHLAB MOHAMMAD, ALIBAKHSHI MEHDI, MANSOURI MOHAMMAD. NONLINEAR ADAPTIVE NEURAL IDENTIFIER FILTER WITH OPTIMAL LEARNING RATE FOR CONVERGING OF PARAMETERS BASED ON GRADIENT DESCENT. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2015;6(2):75-86. Available from: https://sid.ir/paper/203010/en

IEEE: Copy

FATEMEH ALIBAKHSHI, MOHAMMAD TESHNEHLAB, MEHDI ALIBAKHSHI, and MOHAMMAD MANSOURI, “NONLINEAR ADAPTIVE NEURAL IDENTIFIER FILTER WITH OPTIMAL LEARNING RATE FOR CONVERGING OF PARAMETERS BASED ON GRADIENT DESCENT,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 6, no. 2, pp. 75–86, 2015, [Online]. Available: https://sid.ir/paper/203010/en

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