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Linearly graded behavioural analogue performance models using support vector machines and VHDL-AMS

Linearly graded behavioural analogue performance models using support vector machines and VHDL-AMS
Linearly graded behavioural analogue performance models using support vector machines and VHDL-AMS
A concept of linearly graded statistical models for analogue performance evaluation is proposed and a suitable technique for automatic generation of analogue performance models using support vector machines is presented. The analogue system's behaviour is specified using VHDL-AMS descriptions. Practical application of the technique is demonstrated with a case study of automated analogue performance model generation for an analogue filter.
Ren, Xianqiang
c7f83e76-c48c-4690-86e3-f5fddb5e2198
Kazmierski, Tom
a97d7958-40c3-413f-924d-84545216092a
Ren, Xianqiang
c7f83e76-c48c-4690-86e3-f5fddb5e2198
Kazmierski, Tom
a97d7958-40c3-413f-924d-84545216092a

Ren, Xianqiang and Kazmierski, Tom (2005) Linearly graded behavioural analogue performance models using support vector machines and VHDL-AMS. Forum on specification and design languages, Lausanne, Switzerland.

Record type: Conference or Workshop Item (Other)

Abstract

A concept of linearly graded statistical models for analogue performance evaluation is proposed and a suitable technique for automatic generation of analogue performance models using support vector machines is presented. The analogue system's behaviour is specified using VHDL-AMS descriptions. Practical application of the technique is demonstrated with a case study of automated analogue performance model generation for an analogue filter.

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More information

Published date: 2005
Additional Information: Event Dates: 2005.09.27 - 2005.09.30
Venue - Dates: Forum on specification and design languages, Lausanne, Switzerland, 2005-01-01
Organisations: EEE

Identifiers

Local EPrints ID: 264618
URI: http://eprints.soton.ac.uk/id/eprint/264618
PURE UUID: 43095816-c814-4642-9cc2-18df6e0f99b6

Catalogue record

Date deposited: 03 Oct 2007
Last modified: 10 Dec 2021 21:48

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Contributors

Author: Xianqiang Ren
Author: Tom Kazmierski

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