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Adaptive neuro-fuzzy inference system for modelling and control

Proceedings First International IEEE Symposium Intelligent Systems, 2003
A new approach for an adaptive neuro-fuzzy inference system for modeling and control is proposed. This approach uses a general regression neural network with a different learning capability from the classical clustering algorithm normally used by this specific network.
T.G.B. Amaral   +2 more
openaire   +1 more source

Bayesian inference using an adaptive neuro-fuzzy inference system

Fuzzy Sets and Systems, 2023
Mohammed Knaiber, Leen Alawieh
openaire   +1 more source

Breast Tumors Classification Using Adaptive Neuro-Fuzzy Inference System

Journal of Clinical Engineering, 2017
Breast cancer is one of the world's leading causes of cancer-related deaths and ranks second in the cancer fact sheets. In Sudan, the increasing incidence, detection at late stages, and early onset of the disease make early detection and diagnosis of breast cancer an overbearing task.
Sahar Haj Ali A. Mohammed   +1 more
openaire   +1 more source

Analog VLSI implementation of adaptive neuro-fuzzy inference systems

ICECS 2000. 7th IEEE International Conference on Electronics, Circuits and Systems (Cat. No.00EX445), 2002
This paper presents an analog VLSI implementation of adaptive neuro-fuzzy inference systems (ANFIS). Stochastic perturbative techniques, which are more VLSI friendly than standard learning techniques such as back-propagation, are used for on-chip learning. The system is tested by the task of predicting the Mackey-Glass chaotic time series.
A. Sultan, M. El-Sayed
openaire   +1 more source

Self-adaptive neuro-fuzzy inference systems for classification applications

IEEE Transactions on Fuzzy Systems, 2002
This paper presents a self-adaptive neuro-fuzzy inference system (SANFIS) that is capable of self-adapting and self-organizing its internal structure to acquire a parsimonious rule-base for interpreting the embedded knowledge of a system from the given training data set.
null Jeen-Shing Wang, C.S.G. Lee
openaire   +1 more source

Extraction of Fetal Electrocardiogram Using Adaptive Neuro-Fuzzy Inference Systems

IEEE Transactions on Biomedical Engineering, 2007
In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it ...
openaire   +2 more sources

Adaptive Neuro-Fuzzy Inference Systems vs. Stochastic Models for Mortality Data

2014
A comparative analysis is done between stochastic models and Adaptive Neuro–Fuzzy Inference System applied to the projection of the longevity trend. The stochastic models provides the heuristic rule for obtaining projections. In the context of ANFIS models, the fuzzy logic allows for determining the learning algorithm on the basis of the relationship ...
RUSSOLILLO, Maria   +2 more
openaire   +4 more sources

Automatic diagnosis of diabetes using adaptive neuro‐fuzzy inference systems

Expert Systems, 2010
Abstract: A new approach based on an adaptive neuro‐fuzzy inference system (ANFIS) is presented for diagnosis of diabetes diseases. The Pima Indians diabetes data set contains records of patients with known diagnosis. The ANFIS classifiers learn how to differentiate a new case in the domain by being given a training set of such records.
openaire   +2 more sources

Adaptive Neuro-Fuzzy Inference Systems for Extracting Fetal Electrocardiogram

2006 IEEE International Symposium on Signal Processing and Information Technology, 2006
In this paper, we present an efficient technique for extracting the fetal electrocardiogram (FECG) from a composite ECG recording. Our technique uses an adaptive neuro-fuzzy inference system (ANFIS) that operates on two ECG signals recorded at the thoracic and abdominal areas of the mother's skin.
openaire   +1 more source

Causal inference for time series

Nature Reviews Earth & Environment, 2023
Jakob Runge, Gherardo Varando
exaly  

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