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- W2556772904 abstract "Mental models are pen pictures that are used to represent complex systems and their aspects including views of different system stakeholders. They are used extensively in complex system modelling and play a critical role in model development. In this paper we investigate an echo state network (ESN) encoded with limited number of reservoir nodes to automatically learn mental models through the use of system observations. Three different evolutionary algorithms, genetic algorithm (GA), differential evolution (DE) and particle swarm optimisation (PSO) are used to optimise the weights and design parameters of ESN in order to learn models closer to manually developed models. Such an approach can be useful in reducing the modelling effort required by human modellers as well as the subjective bias in model development. The empirical analysis using two case studies shows that the ESN encoded with restricted number of nodes and optimised by different evolutionary algorithms guided with a combined fitness function that takes both output error and reservoir connectivity into account is able to learn similar structure as original mental models as well as is able to generate the correct output behaviour." @default.
- W2556772904 created "2016-11-30" @default.
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- W2556772904 date "2016-07-01" @default.
- W2556772904 modified "2023-10-16" @default.
- W2556772904 title "Optimising a constrained echo state network using evolutionary algorithms for learning mental models of complex dynamical systems" @default.
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- W2556772904 doi "https://doi.org/10.1109/ijcnn.2016.7727822" @default.
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