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NEURO-FUZZY SYSTEMS: Learning Models
International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06), 2006The goal of this research is the analysis of learning models by using of arithmetic operations applied in a neuro-fuzzy system (NFS). The research integrates the concepts between artificial neural network (ANN) and the fuzzy sets theory (FST). In order to assess the validity of the proposal, an FNS is proposed to diagnose paroxysmal events involving ...
Lucimar F. de Carvalho +4 more
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Proceedings of 19th Convention of Electrical and Electronics Engineers in Israel, 2002
The concept of fuzzy logic has been incorporated into the neural network so as to enable a system to deal with cognitive uncertainties in a manner more like humans. This integration yields the neuro fuzzy system, that captures the benefits of the fuzzy logic as well as the neural network tools into a single approach. Many neuro fuzzy-based applications
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The concept of fuzzy logic has been incorporated into the neural network so as to enable a system to deal with cognitive uncertainties in a manner more like humans. This integration yields the neuro fuzzy system, that captures the benefits of the fuzzy logic as well as the neural network tools into a single approach. Many neuro fuzzy-based applications
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Merging Ensemble of Neuro-Fuzzy Systems
2006 IEEE International Conference on Fuzzy Systems, 2006Classification accuracy is nearly always improved after combining many systems. One of the most popular methods of multiple classification is boosting. In the paper we develop a method for merging fuzzy rule bases of neuro-fuzzy systems constituting an ensemble trained by the boosting algorithm.
Marcin Korytkowski +3 more
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2000
There are generally three approaches to building mathematical models: white box modeling, where everything is considered to be known from physical laws, black box modeling (system identification), where all knowledge derives from measurements, gray box modeling, where both physical laws and observed measurements are used to design a ...
Ernest Czogała, Jacek Łęski
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There are generally three approaches to building mathematical models: white box modeling, where everything is considered to be known from physical laws, black box modeling (system identification), where all knowledge derives from measurements, gray box modeling, where both physical laws and observed measurements are used to design a ...
Ernest Czogała, Jacek Łęski
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A general approach to neuro-fuzzy systems
10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002Presents a general approach to neuro fuzzy systems. It includes both Mamdani (constructive) and logical (destructive) fuzzy inference. Moreover, a new class of fuzzy inference (and corresponding neuro-fuzzy systems) is introduced.
Leszek Rutkowski, Krzysztof Cpalka
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On Automatic Design of Neuro-fuzzy Systems
2010In this paper we propose a new approach for automatic design of neuro-fuzzy systems. We apply evolutionary strategy to determine the number of rules, number of antecedents, number of inputs, and number of discretization points of neuro-fuzzy systems. Proper selection of these elements influences the accuracy of the system and its interpretability.
Krzysztof Cpalka +2 more
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Rough-Neuro-Fuzzy Systems for Classification
2007 IEEE Symposium on Foundations of Computational Intelligence, 2007In the paper we present flexible neuro-fuzzy systems and a method for their reduction. The method is based on the concept of the weighted triangular norms. Moreover, a rough-neuro-fuzzy classifier working in the case of missing features is described.
Krzysztof Cpalka +2 more
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A self-learning neuro-fuzzy system
1995Often a major difficulty in the design of rule-based expert systems is the process of acquiring the requisite knowledge in the form of production rules. In most of expert systems, crisp or fuzzy if-then rules are generally derived from human experts using linguistic information.
Nicholas DeClaris, Mu-Chun Su
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On the synergism of evolutionary neuro-fuzzy system
2016 International Joint Conference on Neural Networks (IJCNN), 2016In the recent past, it has been seen that the synergism of evolutionary, fuzzy and neural network is gaining popularity over individual techniques due to its combined computational efficiency. In this paper, we have investigated the feasibility of synergism between evolutionary fuzzy clustering using three different validity criteria and neural ...
Vivek Srivastava +3 more
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NEURO-FUZZY STRUCTURES IN FDI SYSTEM
IFAC Proceedings Volumes, 2002Abstract Fault diagnosis systems have an important role in industrial plants because the early fault detection and isolation (FDI) can minimize damages in the plants. The main aim of this work is to propose a two-stage neuro-fuzzy approach as a fault diagnosis system in dynamic processes.
Mendes, Mário J. G. C. +3 more
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