Adaptive Fuzzy Morphological Filtering of Impulse Noisein Images
Multidimensional Systems and Signal Processing
On Endmember Detection in Hyperspectral Images with Morphological Associative Memories
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
On the Computational Power of Max-Min Propagation Neural Networks
Neural Processing Letters
Robust asymptotic stability of uncertain fuzzy BAM neural networks with time-varying delays
Fuzzy Sets and Systems
Global exponential stability analysis of fuzzy BAM neural networks with time-varying delays
International Journal of Hybrid Intelligent Systems
A Robust High-Order Mixed L2-Linfty Estimation for Linear-in-the-Parameters Models
Journal of Scientific Computing
An improvement on genetic-based learning method for fuzzy artificial neural networks
Applied Soft Computing
A gradient-descent-based approach for transparent linguistic interface generation in fuzzy models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A novel fuzzy BP learning algorithm for four-layer regular fuzzy neural networks
CIMMACS'05 Proceedings of the 4th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
The learning algorithm for a novel fuzzy neural network
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
| Hi-index | 0.00 |
This paper discusses the Δ-rule and training of min-max neural networks by developing a differentiation theory for min-max functions, the functions containing min (∧) and/or max (V) operations. We first prove that under certain conditions all min-max functions are continuously differentiable almost everywhere in the real number field ℜ and derive the explicit formulas for the differentiation. These results are the basis for developing the Δ-rule for the training of min-max neural networks. The convergence of the new Δ-rule is proved theoretically using the stochastic theory, and is demonstrated with a simulation example