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dc.contributor.authorPurvis, Martinen_NZ
dc.contributor.authorKasabov, Nikolaen_NZ
dc.contributor.authorBenwell, George Len_NZ
dc.contributor.authorZhou, Qing Qingen_NZ
dc.contributor.authorZhang, Fengen_NZ
dc.date.available2011-04-07T03:06:36Z
dc.date.copyright1998-12en_NZ
dc.identifier.citationPurvis, M., Kasabov, N., Benwell, G. L., Zhou, Q. Q., & Zhang, F. (1998). Neuro-fuzzy methods for environmental modelling (Information Science Discussion Papers Series No. 98/10). University of Otago. Retrieved from http://hdl.handle.net/10523/1113en
dc.identifier.urihttp://hdl.handle.net/10523/1113
dc.description.abstractThis paper describes combined approaches of data preparation, neural network analysis, and fuzzy inferencing techniques (which we collectively call neuro-fuzzy engineering) to the problem of environmental modelling. The overall neuro-fuzzy architecture is presented, and specific issues associated with environmental modelling are discussed. A case study that shows how these techniques can be combined is presented for illustration. We also describe our current software implementation that incorporates neuro-fuzzy analytical tools into commercially available geographical information system software.en_NZ
dc.format.mimetypeapplication/pdf
dc.publisherUniversity of Otagoen_NZ
dc.relation.ispartofseriesInformation Science Discussion Papers Seriesen_NZ
dc.subject.lcshQA76 Computer softwareen_NZ
dc.titleNeuro-fuzzy methods for environmental modellingen_NZ
dc.typeDiscussion Paperen_NZ
dc.description.versionUnpublisheden_NZ
otago.bitstream.pages18en_NZ
otago.date.accession2011-01-12 02:36:24en_NZ
otago.schoolInformation Scienceen_NZ
otago.openaccessOpen
otago.place.publicationDunedin, New Zealanden_NZ
dc.identifier.eprints1024en_NZ
otago.school.eprintsKnowledge Engineering Laboratoryen_NZ
otago.school.eprintsInformation Scienceen_NZ
dc.description.references[1] N. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, Cambridge, MA, 1996. [2] X. Zhuang and B. A. Engel, "Classification of Multispectral Remote Sensing Data Using Neural Networks vs. Statistical Methods", Proceedings of the International Winter Meetings of the American Society of Agricultural Engineers, Chicago, 1990. [3] S.-I. Horikawa, T. Furuhashi, and Y. Uchikasa, "On Fuzzy Modelling Using Fuzzy Neural Networks with Back-Propagation Algorithm", IEEE Transactions on Neural Networks, 3(5), 801-806, 1992 [4] M. Gupta and D. H. Rao, "On the Principles of Fuzzy Neural Networks", Fuzzy Sets and Systems, 61(1), 1-18, 1994. [5] D. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989. [6] H. Liu and R. Setiono, "A Probabilistic Approach to Feature Selection -- A Filter Solution". Machine Learning, Proc. of the 13th International Conference, Bari, Italy, 319-327, 1996. [7] H. Almuallim and T. G. Dietterich, "Learning Boolean Concepts in the Presence of Many Irrelevant Features", Artificial Intelligence, 69(1-2):279-305, 1994. [8] K. Kira and L. A. Rendell,"The Feature Selection Problem: Traditional Methods and a New Algorithm", AAAI-92, Proceedings of the Ninth National Conference on Artificial Intelligence, AAAI Press, 123-128, 1992. [9] H. Liu and R. Setiono. "Chi2: Feature Selection and Discretization of Numeric Attributes", Proceedings of the 7th International Conference on Tools with Artificial Intelligence, Washington D.C., 388-391, 1995. [10] R. Fisher, "The Use of Multiple Measurements in Taxonomic Problems", Ann. Eugenics, 7:179-188, 1936. [11] M. K. Purvis, N. K. Kasabov, F. Zhang, and G. L. Benwell, "Connectionist-Based Methods for Knowledge Acquisition from Spatial Data", Proceedings of the IASTED International Conference on Advanced Technology in the Environmental Field,Gold Coast, Australia, 151-154, 1996. [12] Environmental Systems Research Institute, Inc., Redlands CA, 1996. [13] R. Setiono. "A Penalty-function Approach for Pruning Feedforward Neural Networks", Neural Computation, 1997, Vol. 9, No. 1, 301-320, 1997. [14] R. Setiono. "Extracting Rules from Neural Networks by Pruning and Hidden-unit Splitting", Neural Computation, 1997, Vol. 9, No. 1, 321-341, 1997.en_NZ
otago.relation.number98/10en_NZ
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