Abstract
The author proposed a novel approach for evolving the architecture of a multi-layer neural network based on neural network and fuzzy logic technologies. The model is front-network which comprised with five layers architecture which composed of dynamic inference of fuzzy rules where the consequent sub-models are implemented by recurrent neural networks with internal feedback paths and dynamic neuron synapses. An optimal learning scheme with the evaluation guide line which error data embed is applied for training of LF-DFNN models. The results of experiment demonstrate that new model have superior performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Promcharoen, S., Rangsanseri, Y., Ongsomwang, S., Jaruppat, J.: Supervised Classification of Multispectral Satellite Images using Fuzzy Logic and Neural Network, Hong Kong, China (1999)
Kulkarni, A.D.: Neural-fuzzy models for multispectral image analysis. International Journal of Applied Intelligence 8, 173–187 (1998)
Oh, S., Roh, S., Kim, Y.: Design of Genetic Fuzzy Set-Based Polynomial Neural Networks with the Aid of Information Granulation. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 428–433. Springer, Heidelberg (2005)
Gedeon, T.D.: A Hybrid Bidirectional Network. In: Proceedings of 2nd International Symposium of Hungarian Researchers on Computational Intelligence, Budapest, Hungary, pp. 115–124 (November 2001)
Oh, S.K., Pedrycz, W., Park, B.J.: Self-organizing neurofuzzy networks based on evolutionary fuzzy granulation. IEEE Transactions on Systems, Man, and Cybernetics – Part A: System and Humans 33 (2003)
Melin, P., Mancilla, A., Lopez, M., Mendoza, O.: A hybrid modular neural network architecture with fuzzy Sugeno integration for time series forecasting. Applied Soft Computing 7, 1217–1226 (2007)
Castillo, O., Melin, P.: Automated mathematical modelling for financial time series prediction combining fuzzy logic and fractal theory, Soft Computing for Financial Engineering, pp. 93–106. Springer, Germany (1999)
Jakubeka, S., Keuth, N.: A local neuro-fuzzy network for high-dimensional models and optimization. Engineering Applications of Artificial Intelligence 19, 705–717 (2006)
McBratney, A.B., Odeh, I.O.A.: Application of fuzzy sets in soil science: Fuzzy logic, fuzzy measurement and fuzzy decisions. Geoderma 77, 85–113 (1997)
Schdmit, A., Bandar, Z.: A modular neural network architecture with additional generalization abilities for high dimensional input vectors. In: Proceedings of ICANNGA 1997, Norwich, England (1997)
Becerikli, Y., Konar, A.F., Samad, T.: Intelligent optimal control with dynamic neural networks. Neural Networks 16, 251–259 (2003)
Yi, S., et al.: Global optimization for NN training. IEEE computer 3, 45–54 (1996)
Yaolin, L., Limin, J.: Model of Land Suitability Evaluation Based on Computational Intelligence. Geomatics and Information Science of Wuhan University 30(04), 283–288 (2005)
Guler, I., Ubeyli, E.D.: A mixture of experts network structure for modeling Doppler ultrasound blood flow-signals. Computers in Biology and Medicine 35, 565–582 (2004b)
Lucek, P., Hanke, J., Reich, J., Solla, S.A., Ott, J.: Multi-locus nonparametric linkage analysis of complex trait loci with neural networks. Hum. Hered. 48, 275–284 (1998)
Park, B.J., Pedrycz, W., Oh, S.K.: Fuzzy Polynomial Neural Networks: Hybrid Architectures of Fuzzy Modeling. IEEE Transaction on Fuzzy Systems 10, 607–621 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miao, Z., Xu, H., Wang, X. (2007). The Modified Self-organizing Fuzzy Neural Network Model for Adaptability Evaluation. In: Li, K., Li, X., Irwin, G.W., He, G. (eds) Life System Modeling and Simulation. LSMS 2007. Lecture Notes in Computer Science(), vol 4689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74771-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-540-74771-0_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74770-3
Online ISBN: 978-3-540-74771-0
eBook Packages: Computer ScienceComputer Science (R0)