Abstract
Data modelling is a broad area with many applications, and is concerned with capturing the relationships of complex systems from observations made on them. A cyclic construction approach to data modelling is advocated here, based on a designtrain-validate-interpret cycle. Traditional approaches to data modelling with neural networks typically produce opaque systems which are difficult to interpret and hence validate. Neurofuzzy systems equip neural networks with a linguistic interpretation which provides the designer with enhanced transparency enabling the loop to be closed in the modelling cycle.
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Kárný, M., Warwick, K., Kůrková, V. (1998). Neurofuzzy Systems Modelling: A Transparent Approach. In: Kárný, M., Warwick, K., Kůrková, V. (eds) Dealing with Complexity. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1523-6_8
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DOI: https://doi.org/10.1007/978-1-4471-1523-6_8
Publisher Name: Springer, London
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