Lyapunov theory-based multilayered neural network

King Hann Lim, Kah Phooi Seng, Li Minn Ang, Siew Wen Chin

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)


    This brief presents a Lyapunov theory-based weight adaptation scheme for a multilayered neural network (MLNN) mainly used to classify a multiple-input-multiple-output (MIMO) problem. Initially, the MLNN system is linearized using Taylor series expansion. Then, the weight adaptation scheme is designed based on the Lyapunov stability theory to iteratively update the weight. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Hence, the Lyapunov theory-based MLNN acts as a MIMO classifier for face recognition. Analysis and discussion on Lyapunov properties of the proposed classifier are included. The performance of the proposed technique is tested on the Olivetti Research Laboratory database for face classification, and some comparisons with existing conventional techniques are given. Simulation results have revealed that our proposed system achieved better performance.

    Original languageEnglish
    Pages (from-to)305-309
    Number of pages5
    JournalIEEE Transactions on Circuits and Systems II: Express Briefs
    Issue number4
    Publication statusPublished - 23 Mar 2009


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