Fuzzy adaptive network in presidential elections

  • Authors:

  • Yue Jiao;Yu-Ru Syau;E. Stanley Lee

  • Affiliations:

  • The School for Marine Science and Technology, University of Massachusetts, Dartmouth, New Bedford, MA 02744-1221, United States;Department of Information Management, National Formosa University, Yunlin 632, Taiwan;Department of Industrial and Manufacturing Systems Engineering, Kansas State University, Manhattan, KS 66506, United States

  • Venue:
  • Mathematical and Computer Modelling: An International Journal
  • Year:
  • 2006

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Abstract

Several scientific forecasting models for presidential elections have been suggested. However, most of these models are based on traditional statistics approaches. Since the system is linguistic, vague, and dynamic in nature, the traditional rigorous mathematical approaches are inappropriate for the modeling of this kind of humanistic system. This paper presents a combined neural fuzzy approach, namely a fuzzy adaptive network, to model and forecast the problem of a presidential election. The fuzzy adaptive network, which is ideally suited for the modeling of vaguely defined humanistic systems, combines the advantages of the representation ability of fuzzy sets and the learning ability of a neural network. To illustrate the approach, experiments were carried out by first formulating the problem, then training the network, and, finally, predicting the election results based on the trained network. The experimental results show that a fuzzy adaptive network is an ideal approach for the modeling and forecasting of national presidential elections.