QML-Morven

A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches

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3 Citations (Scopus)

Abstract

In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.
Original languageEnglish
Pages (from-to)795–808
Number of pages13
JournalJournal of Computational Science
Volume5
Issue number5
Early online date18 Jun 2014
DOIs
Publication statusPublished - Sep 2014

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Differential equations
Differential equation
Scalability
Learning Strategies
Model
Series
Experiments
Requirements
Experiment
Learning
Framework
Influence

Keywords

  • qualitative reasoning
  • learning qualitative differential equation models
  • artificial immune systems
  • backtrackign with forward checking

Cite this

@article{e69d0e155cf94138b1bbe6875628b97b,
title = "QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches",
abstract = "In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.",
keywords = "qualitative reasoning, learning qualitative differential equation models, artificial immune systems, backtrackign with forward checking",
author = "Wei Pang and Coghill, {George MacLeod}",
note = "Article Accepted Date: 1 June 2014",
year = "2014",
month = "9",
doi = "10.1016/j.jocs.2014.06.002",
language = "English",
volume = "5",
pages = "795–808",
journal = "Journal of Computational Science",
issn = "1877-7503",
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number = "5",

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AU - Pang, Wei

AU - Coghill, George MacLeod

N1 - Article Accepted Date: 1 June 2014

PY - 2014/9

Y1 - 2014/9

N2 - In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.

AB - In this paper, a novel qualitative differential equation model learning (QML) framework named QML-Morven is presented. QML-Morven employs both symbolic and evolutionary approaches as its learning strategies to deal with models of different complexity. Based on this framework, a series of experiments were designed and carried out to: (1) investigate factors that influence the learning precision and minimum data requirement for successful learning; (2) address the scalability issue of QML systems.

KW - qualitative reasoning

KW - learning qualitative differential equation models

KW - artificial immune systems

KW - backtrackign with forward checking

U2 - 10.1016/j.jocs.2014.06.002

DO - 10.1016/j.jocs.2014.06.002

M3 - Article

VL - 5

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EP - 808

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JF - Journal of Computational Science

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