Short-term MPEG-4 video traffic prediction using ANFIS
International Journal of Network Management
Fuzzy modeling of fluidized catalytic cracking unit
Applied Soft Computing
Nonlinear active noise control using EKF-based recurrent fuzzy neural networks
International Journal of Hybrid Intelligent Systems
Filtered-X Adaptive Neuro-Fuzzy Inference Systems for Nonlinear Active Noise Control
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
From minimum enclosing ball to fast fuzzy inference system training on large datasets
IEEE Transactions on Fuzzy Systems
Adaptive non-additive generalized fuzzy systems
Applied Soft Computing
Additive and nonadditive fuzzy hidden Markov models
IEEE Transactions on Fuzzy Systems
Multioutput adaptive neuro-fuzzy inference system
NN'10/EC'10/FS'10 Proceedings of the 11th WSEAS international conference on nural networks and 11th WSEAS international conference on evolutionary computing and 11th WSEAS international conference on Fuzzy systems
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Estimation of fuzzy measures using covariance matrices in Gaussian mixtures
Applied Computational Intelligence and Soft Computing
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part II
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The adaptive network-based fuzzy inference systems (ANFIS) of Jang (1993) is extended to the generalized ANFIS (GANFIS) by proposing a generalized fuzzy model (GFM) and considering a generalized radial basis function (GRBF) network. The GFM encompasses both the Takagi-Sugeno (TS)-model and the compositional rule of inference (CRI) model. The conditions by which the proposed GFM converts to TS-model or the CRI-model are presented. The basis function in GRBF is a generalized Gaussian function of three parameters. The architecture of the GRBF network is devised to learn the parameters of GFM, where the GRBF network and GFM have been proved to be functionally equivalent. It Is shown that GRBF network can be reduced to either the standard RBF or the Hunt's RBF network. The issue of the normalized versus the non-normalized GRBF networks is investigated in the context of GANFIS. An interesting property of symmetry on the error surface of GRBF network is investigated. The proposed GANFIS is applied to the modeling of a multivariable system like stock market.