A NEURAL NETWORK APPROACH TO DETECT AND SINGLE FAULTS IN NONLINEAR SYSTEMS

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MOUSAVINIA A.; MOHAMMADI K.

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Paper Information

Journal: Scientia Iranica Year:2002 | Volume:9 | Issue:4 (ELECTRICAL ENGINEERING) Page(s): 336-345

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Title

A NEURAL NETWORK APPROACH TO DETECT AND SINGLE FAULTS IN NONLINEAR SYSTEMS

Author(s)

MOUSAVINIA A. | MOHAMMADI K. | Issue Writer Certificate

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Abstract

In this paper, a new faul tolerant feed forward neural network architecture is first introduced, using fault tolerant neurl nodes in its output layer, which is the most critical layer in the network. Then, the conventional Back error Propagation (BP) algorithm is modified to apply to this architecture with he least learning degradation for fault tolerant nodes. The presented neural network, jointed with a systematic real convolutional code, contributes in an Algorithm Based Fault Tolerant (ABFT) scheme to protect a nonlinear data process block. The neural network is trained to pro uce an all zero syndrome sequence in the absence of any faults. The convolutional code guarantees that faults, representing errors in the processed data, will result in notable nonzero values in syndrome sequence. A majority logic decoder can easily detect and correct single faults by 0 serving the syndrome sequence. Simulation results for random s-a-0 faults, demonstrating error detection and correction behavior, are also presented.

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APA: Copy

MOUSAVINIA, A., & MOHAMMADI, K.. (2002). A NEURAL NETWORK APPROACH TO DETECT AND SINGLE FAULTS IN NONLINEAR SYSTEMS. SCIENTIA IRANICA, 9(4 (ELECTRICAL ENGINEERING)), 336-345. SID. https://sid.ir/paper/289501/en

Vancouver: Copy

MOUSAVINIA A., MOHAMMADI K.. A NEURAL NETWORK APPROACH TO DETECT AND SINGLE FAULTS IN NONLINEAR SYSTEMS. SCIENTIA IRANICA[Internet]. 2002;9(4 (ELECTRICAL ENGINEERING)):336-345. Available from: https://sid.ir/paper/289501/en

IEEE: Copy

A. MOUSAVINIA, and K. MOHAMMADI, “A NEURAL NETWORK APPROACH TO DETECT AND SINGLE FAULTS IN NONLINEAR SYSTEMS,” SCIENTIA IRANICA, vol. 9, no. 4 (ELECTRICAL ENGINEERING), pp. 336–345, 2002, [Online]. Available: https://sid.ir/paper/289501/en

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