Characteristics of the functional link net: a higher order delta rule net

Klassen,; Pao,; Chen, Guihai

doi:10.1109/icnn.1988.23885

Cited by 92 publications

(38 citation statements)

References 2 publications

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“…If we x the membership functions and adapt only the consequent part, then ANFIS can be viewed as a functional-link network 46], 71] where the \enhanced representations" of the input variables are obtained via the membership functions. These \enhanced representations", which take advantage of human knowledge, apparently express more insight than the functional expansion and the tensor (outer product) models 71].…”

Section: B Hybrid Learning Algorithmmentioning

confidence: 99%

Neuro-fuzzy modeling and control

1995

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Abstract| F undamental and advanced developments in neuro-fuzzy synergisms for modeling and control are reviewed. The essential part of neuro-fuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive n e t works is called ANFIS (Adaptive-Network-based Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neuro-fuzzy approaches are also addressed.

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Section: B Hybrid Learning Algorithmmentioning

confidence: 99%

Neuro-fuzzy modeling and control

1995

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“…For a desired output, the network learns from many inputs. It is a supervised learning method, and is a generalization of the delta rule (a kind of learning rule in artificial neural network method) (Klassen et al, 1988). It requires a dataset of the desired output for many inputs, making up the training set.…”

Section: Upa-cen Relationship Simulationmentioning

confidence: 99%

Urban population agglomeration in view of complex ecological niche: A case study on Chinese prefecture cities

Han

Wang

et al. 2014

Ecological Indicators

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“…Functional links were originally proposed as a method for providing more information to a neural network (Klassen, 1988). By applying non-linear operators to the input, the data complexity increases while the network complexity decreases.…”

Section: Vector Quantizationmentioning

confidence: 99%