Performance Analysis of Evolving Fuzzy Neural Networks for Pattern Recognition

• Autores: Ronei Marcos De Moraes
• Localización: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology, ISSN-e 1134-5632, Vol. 20, Nº. 1, 2013, págs. 63-69
• Idioma: inglés
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• Resumen

  • The Evolving Fuzzy Neural Networks (EFuNNs), recently proposed by Kasabov are dynamic connectionist feed forward networks with five layers of neurons and they are adaptive rule-based systems. Several paper can be found in the literature comparing EFuNN with other methods. However, it is known that results of all pattern recognition methods depend on the training data and in particular from statistical distribution of training data. This work assesses the accuracy of EFuNNs) for pattern recognition tasks using seven different statistical distributions data. Results of assessment are provided and show different accuracy according to the statistical distribution of data.

• Referencias bibliográficas
  • Cohen, J., A coeficient of agreement for nominal scales. Educational and Psychological Measurement, v.20, p.37-46, 1960.
  • Duda, R. O.; Hart, P. E.; Stork, D. G., Pattern Classification, 2nd ed, Wiley Interscience, 2000.
  • Feller, W., An Introduction to Probability Theory and its Applications. Vol. 2, 2nd ed., Wiley, 1971.
  • Foody, G. M., Status of Land Cover Classification Accuracy Assessment. Remote Sensing of Environment, v.80, 185-201, 2002.
  • Kasabov, N., ECOS: A Frameworkfor Evolving Connectionist Systems and the ECO Learning Paradigm. In Proc. ICONIP'98. pp.1232-1236, 1998.
  • Kasabov, N., Dynamic Evolving Fuzzy Neural Networks with 'm-out-of-n' Activation Nodes ofr On-line Adaptive Systems. Technical Report...
  • Kasabov, N., Evolving Fuzzy Neural Network for Supervised/Unsupervised On-line, Knowledge-based Learning, IEEE Trans. on Man, Machine and...
  • Kasabov, N., On-line learning, reasoning, rule extraction and aggregation in locally optimized evolving fuzzy neural networks. Neurocomputing,...
  • Klir, G. J.; Yuan, B., Fuzzy Sets and Fuzzy Logic: Theory and Applications. Prentice Hall, 1995.
  • Moraes, R. M., Banon, G. J. F., Sandri, S. A., Expert Systems Architecture for Image Classification Using Mathematical Morphology Operators....
  • Moraes, R. M.; Machado, L. S., Evaluation System Based on EFuNN for On-line Training Evaluation in Virtual Reality. Lecture Notes in Computer...
  • Moraes, R. M., Assessment of EFuNN Accuracy for Pattern Recognition Using Data with Different Statistical Distributions. Proceedings of II...
  • Ng, G. S., Murali, T., Shi, D., Wahab A., Application of EFuNN for the Classification of Handwritten Digits. International Journal of Computers,...
  • Simpson, P., Artificial Neural Systems. Pergamon, 1990.
  • Wang, Z., Bovik, A. C., Mean Squared Error: Love It or Leave It? A new look at signal fidelity measures. IEEE Signal Processing Magazine,...
  • Watts, M.J., A Decade of Kasabov's Evolving Connectionist Systems: A Review. IEEE Transactions on Systems, Man, and Cybernetics, Part...
  • Woodford, B. J., Comparative analysis of the EFuNN and the Support Vector Machine models for the classification of horticulture data. In:...
  • Woodford, B. J.; Deng, D.; Benwell, G. L., A Wavelet-based Neuro-fuzzy system for data mining small image sets. CRPIT '04: Proceedings...
  • Zadeh, L.A., Fuzzy Sets. Information Control, n 8., pp. 338-353, 1965.