“…The GA generates optimal control efforts related to the random initial number of each chosen group as the input data for ANFIS to train Takagi–Sugeno (T–S) fuzzy structure coefficients. Also, three theorems are presented to indicate the positivity, boundedness, and existence of the solutions in the presence of the controller. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The purpose of the study is to examine the usage impact of memory, cache, storage, and bus on CPU performance using the Sugeno type and Mamdani type ANFIS models to determine the state of the computer system. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…RESULTS: Radial strain was lower in the subepicardial layer (33.4 ± 38.6%) compared to the mid-myocardial and subendocardial layers (50.3 ± 37.3% and 53.0 ± 40.0%, respectively, both p < 0.001 vs. subepicardial). In the subendo- and midmyocardium, radial strain was lower in patients with severe AS compared to those with non-severe AS (p < 0.05). …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The structural design of MRD is challenging since conventional quasi-static models rely on the yield stress of magnetorheological fluid (MRF) to reflect the rheological property, which cannot be directly observed and is challenging to calculate. The Takagi–Sugeno (T–S) fuzzy neural network and a unique magnetic circuit computation are offered as a novel quasi-static modeling approach to address the issue. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S) fuzzy time-delay model and linear matrix inequalities (LMIs) technique, a systematic gene circuit design method is proposed to simplify the design procedure. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Methods: A method called “Fuzzy Algorithm for Extraction, Monitoring, and Classification of Infectious Diseases” (FAEMC-ID) was developed by the use of fuzzy modeling of the Takagi-Sugeno-Kang type. In addition to the real-time classification, the method is able to update its vocabulary for new keywords and visualize the classified data on the world map to mark the high risk areas. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Taking network parameters as input features, a Takagi-Sugeno-Kang (TSK) fuzzy model is established to identify AD's EEG signal. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…SUBJECTS AND METHODS: ANFIS is used to train the Sugeno systems using neuro-adaptive learning. The fuzzy inference system in the ANFIS is used to define fuzzy rules for fusion. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The issue of non-fragile observer-based adaptive integral sliding mode control for a class of Takagi–Sugeno (T-S) fuzzy descriptor systems with uncertainties and unmeasurable premise variables is investigated. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…In this paper, we combine linked educational and deprivation data across small areas (median population of 1500), then use a local modelling technique, the Takagi-Sugeno fuzzy system, to predict area educational outcomes at ages 7 and 11. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…According to the two design specifications, a Takagi-Sugeno (T-S) fuzzy model is employed to interpolate several local linear stochastic systems to approximate the nonlinear stochastic metal ion biosensor system so that the multi-objective H(2)/H(∞) design of the metal ion biosensor can be solved by an associated linear matrix inequality (LMI)-constrained multi-objective (MO) design problem. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Methods: The core system of the MDSS is a Takagi-Sugeno type ANFIS model that predicts the glomerular filtration rate (GFR) values as the biological marker of the renal failure. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The validity indices to understand all the COVID-19 clusters' quality are analysed based on the Zahid SC (Separation Compaction) index, Xie-Beni Index, Fukuyama–Sugeno Index, Validity function, PC (performance coefficient), and CE (entropy) indexes. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…In this paper, we propose a method that uses a Sugeno fuzzy integral ensemble of four pre-trained deep learning models, namely, VGG-11, GoogLeNet, SqueezeNet v1.1 and Wide ResNet-50-2, for classification of chest CT-scan images into COVID and Non-COVID categories. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The key contributions of this paper are: (1) a novel neuro-fuzzy system: MultiLexANFIS that takes as its input the positive and negative sentiment scores of tweets computed from multiple lexicons—VADER, AFINN and SentiWordNet, in order to classify the tweets into neutral and non-neutral content, (2) a novel set of 64 rules for the Sugeno-type fuzzy inference system—MultiLexANFIS, (3) single-lexicon-based ANFIS variants to classify tweets when multiple lexicons are not available and (4) comparison of MultiLexANFIS with different fuzzy, non-fuzzy and deep learning state of the art on various benchmark datasets revealing the superiority of our proposed neuro-fuzzy system for social sentiment analysis.…”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…Taking the latent factors of the manifold as independent inputs, a fuzzy system-based Takagi-Sugeno-Kang model is established and further trained to identify visual EEG signals. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…These two mechanisms are employed on three popular CNN models – VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers’ outputs to enhance the detection accuracy further. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…ANFIS is a kind of artificial neural network based on the Takagi–Sugeno fuzzy inference system. The ANFIS algorithm uses the advantages of both fuzzy inference systems and artificial neural networks to improve the effectiveness of algorithms in various applications such as modeling, control and classification. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…The surface roughness prediction was performed using a radial basis function (RBF) artificial neural network (ANN) and a Takagi–Sugeno––Kang (TSK) fuzzy model with subtractive clustering. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso

“…These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation. …”
Enlace del recurso
Enlace del recurso
Enlace del recurso