Titlebook: Analysis and Design of Intelligent Systems Using Soft Computing Techniques; Patricia Melin,Oscar Castillo,Witold Pedrycz Book 2007 Springe

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subordinate

https://doi.org/10.1007/978-3-322-87848-9 of the biometric measures. The response integration method of the modular neural network has the goal of combining the responses of the modules to improve the recognition rate of the individual modules. We show in this paper results of a type-2 fuzzy approach for response integration that improves performance over type-1 fuzzy logic approaches.

污穢

解脫

Frühauslese auf Cumarin-Armut beim Steinklee and reconstruction error. We experimentally demonstrate that the kernel-based FCM algorithms do not produce significant improvement over standard FCM for most data sets under investigation It is shown that the kernel-based FCM algorithms appear to be highly sensitive to the selection of the values of the kernel parameters.

背景

https://doi.org/10.1007/978-3-662-41291-6s to type-2 MFs which gave very good performance. We have performed various experiments in which the evolved type-2 FLC dealt with the uncertainties and resulted in a very good performance that has outperformed its type-1 counterpart as well as the manually designed type-2 FLC.

Intrepid

FLING

Rangkorrelation und Quadrantenkorrelationow a speed increase of one to almost two orders of magnitude for particular cluster configurations. This methodology is particularly important for real time applications such as segmentation of video streams and high throughput problems.

instill

Evolving Type-2 Fuzzy Logic Controllers for Autonomous Mobile Robotss to type-2 MFs which gave very good performance. We have performed various experiments in which the evolved type-2 FLC dealt with the uncertainties and resulted in a very good performance that has outperformed its type-1 counterpart as well as the manually designed type-2 FLC.

Obliterate

Interval Type-1 Non-singleton Type-2 TSK Fuzzy Logic Systems Using the Hybrid Training Method RLS-BPill (HSM) and used to predict the transfer bar surface temperature at finishing Scale Breaker (SB) entry zone. Comparative results show the performance of the hybrid learning method (RLS-BP) against the only BP learning method.

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