Book ChapterDOI
Designing neuro-fuzzy systems through backpropagation
Detlef Nauck,Rudolf Kruse +1 more
- pp 203-228
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TLDR
The goal of neuro-fuzzy combinations is to obtain adaptive systems that can use prior knowledge, and can be interpreted by means of linguistic rules.Abstract:
The goal of neuro-fuzzy combinations is to obtain adaptive systems that can use prior knowledge, and can be interpreted by means of linguistic rules. Neuro-fuzzy models can be divided into cooperative models, which use neural networks to determine fuzzy system parameters, and hybrid models which create a new architecture using concepts from both worlds. Besides this, there are concurrent neural/fuzzy models that use neural networks and fuzzy systems separately. Most approaches adapt the backpropagation learning rule [33] for neural networks. Some of these systems are discussed in the following pages.read more
Citations
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Journal ArticleDOI
A neuro-fuzzy method to learn fuzzy classification rules from data
Detlef Nauck,Rudolf Kruse +1 more
TL;DR: A learning method for fuzzy classification rules is discussed, based on NEFCLASS, a neuro-fuzzy model for pattern classification that is able to derive fuzzy rules from a set of training data very quickly, and tunes them by modifying parameters of membership functions.
Journal ArticleDOI
A new methodology of extraction, optimization and application of crisp and fuzzy logical rules
TL;DR: Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables.
Journal ArticleDOI
Neuro-fuzzy systems for function approximation
Detlef Nauck,Rudolf Kruse +1 more
TL;DR: A neuro-fuzzy architecture for function approximation based on supervised learning that is an extension to the already published NEFCON and NEFCLASS models and can be used for any application based on function approximation.
Journal ArticleDOI
Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study
Rahib H. Abiyev,Okyay Kaynak +1 more
TL;DR: The integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem of effective control of an uncertain system and results in a better performance despite its smaller parameter space.
Journal ArticleDOI
Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: Application for sizing a stand-alone PV system
TL;DR: In this paper, an adaptive neuro-fuzzy inference system (ANFIS) model is presented for estimating sequences of mean monthly clearness index (K ¯ t ) and total solar radiation data in isolated sites based on geographical coordinates.
References
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Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
MonographDOI
Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Journal ArticleDOI
Fuzzy logic in control systems: fuzzy logic controller. II
TL;DR: The basic aspects of the FLC (fuzzy logic controller) decision-making logic are examined and several issues, including the definitions of a fuzzy implication, compositional operators, the interpretations of the sentence connectives 'and' and 'also', and fuzzy inference mechanisms, are investigated.
Journal Article
Fuzzy logic in control systems : fuzzy logic controller. Part II
TL;DR: The fuzzy logic controller (FLC) based on fuzzy logic provides a means of converting a linguistic control strategy based on expert knowledge into an automatic control strategy.