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Anfis: adaptive-network-based fuzzy inference systems
Jang J.S.R.
- Vol. 23, Iss: 3, pp 665-685
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The article was published on 1993-01-01 and is currently open access. It has received 1790 citations till now. The article focuses on the topics: Adaptive neuro fuzzy inference system.read more
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On digital soil mapping
TL;DR: The generic framework, which the authors call the scorpanSSPFe (soil spatial prediction function with spatially autocorrelated errors) method, is particularly relevant for those places where soil resource information is limited.
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An approach to online identification of Takagi-Sugeno fuzzy models
TL;DR: An approach to the online learning of Takagi-Sugeno (TS) type models is proposed, based on a novel learning algorithm that recursively updates TS model structure and parameters by combining supervised and unsupervised learning.
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Functional equivalence between radial basis function networks and fuzzy inference systems
TL;DR: It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of fuzzy inference systems are actually equivalent.
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Self-learning fuzzy controllers based on temporal backpropagation
TL;DR: A generalized control strategy that enhances fuzzy controllers with self-learning capability for achieving prescribed control objectives in a near-optimal manner is presented and the inverted pendulum system is employed as a testbed to demonstrate the effectiveness of the proposed control scheme and the robustness of the acquired fuzzy controller.
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A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
TL;DR: In this paper, three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) were compared for landslide susceptibility mapping at Penang Hill area, Malaysia.