Logic programming and knowledge engineering
Logic programming and knowledge engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering
Fuzzy Control
Priority tasks allocation through the maximum entropy principle
ICAI'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Automation and Information - Volume 8
RTFDF description for ARMA systems
FS'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Fuzzy Systems - Volume 8
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
Evolutive neural fuzzy filtering: real time constrains
ISPRA'09 Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation
Evolutive neural fuzzy filtering: an approach
WSEAS Transactions on Systems and Control
A novel well conditions analysis method through SFPI algorithm
WSEAS Transactions on Systems and Control
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
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In this paper we describe the neural fuzzy filtering properties in real-time sense; giving an approach about the real-time neuro-fuzzy digital filters, defined in acronym form as RTNFDF. This kind of filters require the adaptive inference mechanism into the fuzzy logic structure to deduce the filter answers in order to select the best parameter values into the knowledge base (KB), actualizing the filter weights to give a good enough answers in natural linguistic sense; this require that all of the states bound into RTNFDF time limit as a real-time system, considering the Nyquist criteria. In this paper we characterize the membership functions into the knowledge base in a probabilistic way respect to the rules set decisions without lost its real-time description, performing the RTFNDF. Moreover, the paper describes in schematic sense the neurons set architecture into the filter description. The results expressed in formal sense use the concepts exposed in the papers included into the references. Finally, we present in illustrative manner the RTNFDF operations using as a tool the Matlab© software. Explicitly, the paper has eight sections conformed as follows: 1. Introduction, 2. Neural Architecture, 3. Rule Base Dynamics, 4. Neural Rules, 5. Real-time Descriptions, 6. Restrictions for RTNFDF, 7. Simulation, Conclusions and References.