Abstract
The paper introduces a statistical model and a DENFIS-based model for estimating the potential establishment of a pest insect. They have a common probability evaluation module, but very different clustering and regression modules. The statistical model uses a typical K-means algorithm for data clustering, and a multivariate linear regression to build the estimation function, while the DENFIS-based model uses an evolving clustering method (ECM) and a dynamic evolving neural-fuzzy inference system (DENFIS) respectively. The predictions from these two models were evaluated on the meteorological data compiled from 454 worldwide locations, and the comparative analysis shows advantages of the DENFIS-based model as used for estimating the potential establishment of a pest insect.
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Soltic, S., Pang, S., Kasabov, N., Worner, S., Peackok, L. (2004). Dynamic Neuro-fuzzy Inference and Statistical Models for Risk Analysis of Pest Insect Establishment. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_150
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DOI: https://doi.org/10.1007/978-3-540-30499-9_150
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