<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.</p></abstract>

Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to comprehend and scope the complex phenomena under investigation through an abstract representation but also pave the way for the later development of detailed and higher-resolution simulation models. An evolutionary echo state network-based method for supporting the development of such models, which can help to expedite the generation of alternative models for explaining the underlying phenomena and potentially reduce the manual effort required, is proposed. It relies on a customised echo state neural network for learning sparse conceptual model representations from the observed data. In this paper, three evolutionary algorithms, a genetic algorithm, differential evolution and particle swarm optimisation are applied to optimize the network design in order to improve model learning. The proposed methodology is tested on four examples of problems that represent complex system models in the economic, ecological and physical domains. The empirical analysis shows that the proposed technique can learn models which are both sparse and effective for generating the output that matches the observed behaviour.

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

School of Engineering and Information Technology

University of New South Wales at Canberra

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Conceptual or explanatory models are a key element in the process of complex system modelling. They not only provide an intuitive way for modellers to...

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

Data publikacji: 01 lis 2017

Zakres stron: 133 - 154

Otrzymano: 04 mar 2017

Przyjęty: 29 mar 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0010

Słowa kluczowe
Complex systems modelling, Conceptual models, Causal loop diagrams, Computational intelligence, Echo state networks, Evolutionary algorithms

© 2018 Hassan Abdelbari et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Learning Structures of Conceptual Models from Observed Dynamics Using Evolutionary Echo State Networks

Data publikacji: 01 lis 2017

Zakres stron: 133 - 154

Otrzymano: 04 mar 2017

Przyjęty: 29 mar 2017

DOI: https://doi.org/10.1515/jaiscr-2018-0010

Słowa kluczoweComplex systems modelling, Conceptual models, Causal loop diagrams, Computational intelligence, Echo state networks, Evolutionary algorithms

© 2018 Hassan Abdelbari et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Słowa kluczowe
Complex systems modelling, Conceptual models, Causal loop diagrams, Computational intelligence, Echo state networks, Evolutionary algorithms