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An analysis of the absolute stability for a general class of discrete-time recurrent neural networks (RNN's) is presented. A discrete-time model of RNN's is represented by a set of nonlinear difference equations. Some sufficient conditions for the absolute stability are derived using Ostrowski's theorem and the similarity transformation approach. For a(More)
Some globally asymptotical stability criteria for the equilibrium states of a general class of discrete-time dynamic neural networks with continuous states are presented using a diagonal Lyapunov function approach. The neural networks are assumed to have the asymmetrical weight matrices throughout the paper. The resulting criteria are described by the(More)
A method for estimating the equilibrium capacity of a general class of analog feedback neural networks is presented in this brief paper. Some explicit relationships between upper bound of the number of possible stable equilibria and the network parameters such as self-feedback coefficients, weights, and gains of a feedback neural network are obtained.(More)
With continuing advances in biotechnology and genetic engineering, there has been a dramatic increase in the availability of new biomacromolecules, such as peptides and proteins that have the potential to ameliorate the symptoms of many poorly-treated diseases. Although most of these macromolecular therapeutics exhibit high potency, their large molecular(More)
Protein lysine acetylation is a reversible and dynamic post-translational modification. It plays an important role in regulating diverse cellular processes including chromatin dynamic, metabolic pathways, and transcription in both prokaryotes and eukaryotes. Although studies of lysine acetylome in plants have been reported, the throughput was not high(More)
The conventional dynamic backpropagation (DBP) algorithm proposed by Pineda does not necessarily imply the stability of the dynamic neural model in the sense of Lyapunov during a dynamic weight learning process. A difficulty with the DBP learning process is thus associated with the stability of the equilibrium points which have to be checked by simulating(More)
The problem of learning control for a general class of discrete-time nonlinear systems is addressed in this paper using multilayered neural networks (M"s) with feedforward connections. A suitable extension of the concept of input-output linearization of discrete-time nonlinear systems is used to develop the control schemes for both output tracking and model(More)