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IETE Journal of Research, 1998
Ambiguity is always present in any realistic process. This ambiguity may arise from the interpretation of the data inputs and in the rules used to describe the relationships between the informative attributes. Fuzzy logic provides an inference structure that enables the human reasoning capabilities to be applied to artificial knowledge-based systems ...
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Ambiguity is always present in any realistic process. This ambiguity may arise from the interpretation of the data inputs and in the rules used to describe the relationships between the informative attributes. Fuzzy logic provides an inference structure that enables the human reasoning capabilities to be applied to artificial knowledge-based systems ...
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A biological brain-inspired fuzzy neural network: Fuzzy emotional neural network
Biologically Inspired Cognitive Architectures, 2018Abstract In this paper, a brain-inspired fuzzy emotional neural network (FUZZ-ENN) is proposed for uncertainty prediction tasks in real world applications. In the proposed FUZZ-ENN, amygdala connections are modeled by fuzzy IF-THEN behavioral rules and orbitofrontal module inhibits the amygdala responses in order to decrease the uncertainty.
Ehsan Zamirpour, Mohammad Mosleh
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2019
In the previous chapters, you saw neural networks based on crisp inputs, weights, parameters, etc. But in real-life applications, it’s not necessary that you always get the same kind of inputs. Fuzziness in neural networks results in networks having Fuzzy Signals, Fuzzy Weights, etc., in which case you are dealing with Fuzzy Neural Networks.
Himanshu Singh, Yunis Ahmad Lone
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In the previous chapters, you saw neural networks based on crisp inputs, weights, parameters, etc. But in real-life applications, it’s not necessary that you always get the same kind of inputs. Fuzziness in neural networks results in networks having Fuzzy Signals, Fuzzy Weights, etc., in which case you are dealing with Fuzzy Neural Networks.
Himanshu Singh, Yunis Ahmad Lone
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Fuzzy inference neural network for fuzzy model tuning
IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1996In fuzzy modeling, it is relatively easy to manually define rough fuzzy rules for a target system by intuition. It is, however, time-consuming and difficult to fine-tune them to improve their behavior. This paper describes a tuning method for fuzzy models which is applicable regardless of the form of fuzzy rules and the used defuzzification method. For
Lee, KM +2 more
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Fuzzy Sets and Systems, 1999
This paper presents a practical algorithm for training neural networks with fuzzy number weights, inputs, and outputs. Typically, fuzzy number neural networks are difficult to train because of the many α-cut constraints implied by the fuzzy weights.
James Dunyak, Donald Wunsch
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This paper presents a practical algorithm for training neural networks with fuzzy number weights, inputs, and outputs. Typically, fuzzy number neural networks are difficult to train because of the many α-cut constraints implied by the fuzzy weights.
James Dunyak, Donald Wunsch
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2008
The theory of fuzzy logic, founded by Zadeh (1965), deals with the linguistic notion of graded membership, unlike the computational functions of the digital computer with bivalent propositions. Since mentation and cognitive functions of brains are based on relative grades of information acquired by the natural (biological) sensory systems, fuzzy logic ...
Madan M. Gupta +2 more
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The theory of fuzzy logic, founded by Zadeh (1965), deals with the linguistic notion of graded membership, unlike the computational functions of the digital computer with bivalent propositions. Since mentation and cognitive functions of brains are based on relative grades of information acquired by the natural (biological) sensory systems, fuzzy logic ...
Madan M. Gupta +2 more
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Evolutive fuzzy neural networks
[1992 Proceedings] IEEE International Conference on Fuzzy Systems, 2003The authors describe the combination of fuzzy neural networks with genetic algorithms, producing a flexible and powerful learning paradigm, called evolutive learning. Evolutive learning combines as complementary tools both inductive learning through synaptic weight adjustment and deductive learning through the modification of the network topology to ...
R.J. Machado, A. Freitas da Rocha
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Fuzzy-decision neural networks
IEEE International Conference on Acoustics Speech and Signal Processing, 1993In a decision-based neural network (DBNN), the teacher only tells the correctness of the classification for each training pattern. In dealing with practical classification applications where significant overlap may exist between categories, special care is needed to cope with the marginal training patterns. For these situations, a soft decision is more
J.S. Taur, S.Y. Kung
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Fuzzy Neural-Network-Based Controller
Solid State Phenomena, 2015Using a controller is necessary for any automation system. The controller must be cheap, reliable, user friendly and not cause any problems for inputs and outputs. Classical control systems like proportional integral derivative (PID) put adequate results of linear systems and continuous-time.
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Logic - Oriented Fuzzy Neural Networks
International Journal of Hybrid Intelligent Systems, 2004The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy ...
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