An approximate analytical (structural) superposition in terms of two, or more, "alfa"-circuits of the same topology: Pt.1 - description of the superposition
Emanuel Gluskin

TL;DR
This paper introduces an approximate superposition principle for nonlinear circuits composed of similar conductors with polynomial or quasi-polynomial characteristics, revealing a near-linear map between input and characteristic functions.
Contribution
It generalizes the superposition concept to nonlinear alfa-circuits, showing that the input characteristic of combined circuits closely approximates a linear combination of individual characteristics.
Findings
Superposition holds with high precision in nonlinear alfa-circuits
The input characteristic map is approximately linear for combined circuits
Examples include circuits with quadratic and cubic characteristics
Abstract
One-ports named "f-circuits", composed of similar conductors described by a monotonic polynomial, or quasi-polynomial (i.e. with positive but not necessarily integer, powers) characteristic i = f(v) are studied, focusing on the algebraic map f --> F. Here F(.) is the input conductivity characteristic; i.e., iin = F(vin) is the input current. The "power-law" "alfa-circuit" introduced in [1], for which f(v) ~ v^"alfa", is an important particular case. By means of a generalization of a parallel connection, the f-circuits are constructed from the alfa-circuits of the same topology, with different "alfa", so that the given topology is kept, and 'f' is an additive function of the connection. We observe and consider an associated, generally approximated, but, in all of the cases studied, always high-precision, specific superposition. This superposition is in terms of f --> F, and it means that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsNeural Networks and Applications · Advanced Memory and Neural Computing · Quantum Computing Algorithms and Architecture
