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Linear time-invariant (LTI) systems appear frequently in natural sciences and engineering contexts. Many LTI systems are described by ordinary differential equations (ODEs). For example, biological gene regulation, analog filter circuits, and simple mechanical, electrical, and hydraulic systems can all be described with varying approximations as LTI systems using ODEs. While linearity and time-invariance are straightforward to demonstrate for closed-form system definitions, determining whether an ODE describes a system with LTI properties is less obvious and rarely discussed in depth in the literature. Complications arise due to slightly different definitions of linearity in different contexts. This commentary is intended to provide clarity on this subtle point, and act as an instructional aid or educational supplement.
We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based
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