t time-varying description. For the first time, these traditional LTI models were given solid mathematical groundings with their shortcomings clearly identified. As a second application, the SymbolicHTM algorithm has demonstrated that it is possible to (partially) automate the symbolic analysis of LPTV system behavior. It produces ex- pressions for an LPTV system's input-output HTM elements when given the system's 6.1.2 Modeling oscillator dynamic behavior building block models. For a limited number of internal time-varying feedback loops, these symbolic expressions are quite accurate while only containing a limited number of terms. The algorithm complements existing algorithms for symbolic LTI network analysis [Fern98, Gie91, Lin91] and, for the first time, extends symbolic analysis to- wards (periodically) time-varying systems. SymbolicHTM is intended to support the introduction of HTM-based analysis in every day's circuit design practice. 6.1.2 Modeling oscillator dynamic behavior By proper partitioning of the circuit equations, it has been shown possible to separate the computation of an oscillator's fast-varying periodic steady-state behavior and its slow-varying transient dynamics. The key to understanding oscillator dynamics lies in the observation that slow-varying transients are caused by small (perturbation) terms that push the oscillator's core system from one state to another. Here, a state of the oscillator's core corresponds to one of its fast-varying steady-state solutions. In this work, this understanding was translated into mathematics by means of perturbation techniques and averaging. This has resulted in a general theory that applies to a wide variety of problems involving oscillator dynamic behavior. The practical applications addressed in this work concern the analysis of an oscilla- tor's injection locking and phase noise behavior. Furthermore, we also presented an algorithm for the behavioral modeling of (sets of coupled) harmonic oscillators. applications demonstrate the ease and efficienc