 obtained by replacing nonlinear network elements, e.g. transistors, by a small-signal model. The network behavior is then characterized by symbolic expressions for input-output transfer func- tions, input/output impedances, etc. Early on, it became clear that the number of terms in these symbolic expressions grows exponentially with the network's complexity. In order to deal with this "explosion of terms", people introduced techniques for simplifying the expressions, both after [Gie91], during [Wamb98b] and before [Dae02] generation. A second track of research involved hierarchical circuit analysis, resulting in sequences of expressions [Star80]. Although techniques for LTI symbolic network analysis work fine on relatively small circuits, they lack the ability to capture intermodulations between different signal com- ponents. As such, their range of application excludes mixing behavior, harmonic dis- tortion, etc. 4.3.1.2 Weakly nonlinear circuit analysis In an effort to overcome the limitations of LTI network analysis, attention was focused on the behavior of weakly nonlinear circuits [Verha01, Wamb98a]. This has resulted in methods based on Volterra series and perturbation techniques, methods that operate by an iterative application of linear techniques. However, weakly nonlinear network analysis is not able to distinguish between lin- ear time-varying and truely nonlinear behavior. For example, although one would be tempted to consider a multiplication y(t) = ejkwot X u(t) a nonlinear operation, it was argued in section 3.3.1 of the previous chapter that the principle of superposition still holds, making it a linear operation. Hence, the symbolic computation of the HTM- elements using techniques for nonlinear symbolic LTI network analysis hence does not fully exploit the linear nature of LPTV systems. This makes them suboptimal for this kind of applications. 4.3.1.3 Fitting-based approaches All of the methods discussed SO far are analytical. This implies that they try to solve the system or circuit equations 