[PDF] Top 20 Simplified Interval Type-2 Fuzzy Neural Networks

... Simplified Interval Type-2 Fuzzy Neural Networks Yang-Yin Lin, Shih-Hui Liao, Jyh-Yeong Chang, Member, IEEE, and Chin-Teng Lin, Fellow, IEEE Abstract— This paper describes ... See full document

... the interval T2FNN is adopted in this paper to sim- plify the computational ...of type reduction process, called type-reduced set, possesses more important in- formation than a crisp output of ... See full document

... recurrent type-1 FNNs. The compared feedforward type-1 FNN is a self-constructing neural fuzzy inference net- work (SONFIN) [29], which is a powerful network with both structure and parameter ... See full document

... compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN) is proposed for system modeling and noise cancellation ...uses type-2 fuzzy sets in an ... See full document

... This paper proposes a mutually recurrent interval type-2 neu- ral fuzzy system (MRIT2NFS) for dynamic system identifica- tion. Our approach falls within the second category mentioned ... See full document

... a fuzzy set to represent the relationship between the similar cases and the new problem by applying a fuzzy membership function; and (3) generate a prediction based on the fuzzy set by applying a ... See full document

... realizing fuzzy inference and can be constructed from a set of fuzzy ...be simplified. We show that all the characteristics of the FNN—fuzzy inference, universal approximation, and convergence ... See full document

... for neural networks neural-type systems with interval time-varying ...for neural networks neural- type systems with time-varying delays is obtained, which ... See full document

... for neural networks neural-type systems with interval time-varying ...for neural networks neural- type systems with time-varying delays is obtained, which ... See full document

... VI. C ONCLUSION This study proposes an ANFN-MODE for nonlinear system control. The ANFN-MODE controller adopts a nonlinear com- bination of input variables to the consequent part of fuzzy rules and uses a MODE to ... See full document

... for type-4 traffic with period , because if other traffic with a shorter period is scheduled by the deadline driven algo- rithm and some traffic with period is scheduled by the rate monotonic algorithm, the system ... See full document

... bound; 2) training when the samples size is larger than the upper bound; 3) how to change the capacity of the FNN, when the training process fails to ...the type of training ...the type of input ... See full document

... the proposed control mechanism on the traffic type. Such a transformation can greatly reduce the number of dimensions of the NFCAC scheme and save a large percentage of learning time. Here, we adopt a fuzzy ... See full document

... Although DMHCCA can get better results in compari- son with other learning algorithms, it still has a limitation. Specifically, the number of hierarchical level is only two to execute the training of structure and ... See full document

... sparse fuzzy rule-based ...handling fuzzy rule interpolation in sparse fuzzy rule- based systems based on interval type-2 fuzzy ...handles fuzzy rule interpolation ... See full document

... Abstract—Some fuzzy rule interpolation methods have been presented for sparse fuzzy rule-based systems based on interval type-2 fuzzy ...the fuzzy interpolated result and ... See full document

... However, since the knowledge of fuzzy rules learned by the CBP training is distributed over the weights on the input links of OR nodes, there may be rules with identical antecedents and [r] ... See full document

... of neural networks, such as Hopfield neural networks, cellular neural networks, and Cohen-Grossberg neural networks, has been exten- sively investigated; these ... See full document

... In this paper, we extend the work of [4] to present the concepts of interval-valued fuzzy concept-networks based on [6, 71 and to present an algorithm for find[r] ... See full document

... unsupervised fuzzy reasoning ...unsupervised neural network then are explained in a subsequent ...of fuzzy relationship in UFN increased detection robustness and ... See full document