QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches

Wei Pang; George MacLeod Coghill

doi:10.1016/j.jocs.2014.06.002

QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches

Wei Pang, George MacLeod Coghill

Research output: Contribution to journal › Article › peer-review

4 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 language	English
Pages (from-to)	795-808
Number of pages	14
Journal	Journal of Computational Science
Volume	5
Issue number	5
DOIs	https://doi.org/10.1016/j.jocs.2014.06.002
Publication status	Published - Sept 2014

Keywords

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

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10.1016/j.jocs.2014.06.002

Wei Pang
- W.Panghw.acuk
- School of Mathematical & Computer Sciences - Associate Professor
- School of Mathematical & Computer Sciences, Computer Science - Associate Professor
Person: Academic (Research & Teaching)

Cite this

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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",

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doi = "10.1016/j.jocs.2014.06.002",

language = "English",

volume = "5",

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journal = "Journal of Computational Science",

issn = "1877-7503",

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T2 - A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches

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

SN - 1877-7503

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EP - 808

JO - Journal of Computational Science

JF - Journal of Computational Science

IS - 5

ER -

QML-Morven: A Novel Framework for Learning Qualitative Differential Equation Models using Both Symbolic and Evolutionary Approaches

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