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'''Large-signal modeling''' is a common analysis method used in [[electronic engineering]] to describe nonlinear devices in terms of the underlying [[Nonlinearity|nonlinear]] equations. In [[Electronic circuit|circuit]]s containing nonlinear elements such as [[transistor]]s, [[diode]]s, and [[vacuum tube]]s, under "large signal conditions", AC signals have high enough magnitude that nonlinear effects must be considered.<ref>{{Cite book |last1=Snowden |first1=Christopher M. |url=https://books.google.com/books?id=P4_aBwAAQBAJ&dq=%22Large-signal+model%22+-wikipedia&pg=PA170 |title=Compound Semiconductor Device Modelling |last2=Miles |first2=Robert E. |date=2012-12-06 |publisher=Springer Science & Business Media |isbn=978-1-4471-2048-3 |page=170 |language=en}}</ref>
"Large signal" is the opposite of "[[Small-signal model|small signal]]"
==Differences between Small Signal and Large Signal==
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A large signal model, on the other hand, takes into account the fact that the large signal actually affects the operating point, as well as that elements are non-linear and circuits can be limited by power supply values to avoid variation in operating point. A small signal model ignores simultaneous variations in the gain and supply values.
== Large Signal Models (LSMs) in Artificial Intelligence ==
In the ___domain of artificial (machine) intelligence, Large Signal Models enable human-centric interactions and knowledge discovery of signal data similar to how prompts allow users to query an LLM based on unstructured text from the web. Users can ask general questions about relationships between the focus dataset and results from pre-compiled LSTM built on a signal dataset across a large range of domains. This is achieved by layering in latent pattern detection and knowledge graph-based (KG-based) explainability into an LSTM inference pipeline.
==See also==
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