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Robust stability of Cohen-Grossberg neural networks via state transmission matrix

Zhanshan Wang1, Huaguang Zhang, Wen Yu

  • 1School of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China. zhanshan_wang@163.com

IEEE Transactions on Neural Networks
|January 9, 2009
PubMed

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BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
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The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
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