John K. L. Ho

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—This paper studies the approximation ability of continuous time recurrent neural networks to dynamical time-variant systems. It proves that any finite time trajectory of a given dynam-ical time-variant system can be approximated by the internal state of a continuous-time recurrent neural network. Given several special forms of dynamical time-variant(More)
—This paper presents two-dimensional (2-D) system theory based iterative learning control (ILC) methods for linear continuous multivariable systems with time delays in state or with time delays in input. Necessary and sufficient conditions are given for convergence of the proposed ILC rules. In this paper, we demonstrate that the 2-D linear(More)
In this paper, we consider the problem of in-depth document analysis. In particular, we propose a novel document analysis method, named multidimensional latent semantic analysis (MDLSA), which enables us to mine local information efficiently from a document with respect to term associations and spatial distributions. MDLSA works by first partitioning each(More)
A Quasi-Sliding Mode (QSM) based tracking control method for tackling Multiple-Input Multiple-Output (MIMO) nonlinear continuous-time systems with un-matching system uncertainties and exogenous disturbances is proposed. The presented Repetitive Control (RC) scheme ensures robust system stability when the system is subject to non-periodic uncertainties and(More)