Recent advances in neuro-fuzzy system: A survey
KV Shihabudheen, GN Pillai - Knowledge-Based Systems, 2018 - Elsevier
Neuro-fuzzy systems have attracted the growing interest of researchers in various scientific
and engineering areas due to its effective learning and reasoning capabilities. The neuro …
and engineering areas due to its effective learning and reasoning capabilities. The neuro …
A meta-cognitive interval type-2 fuzzy inference system and its projection based learning algorithm
A meta-cognitive interval type-2 neuro-fuzzy inference system (McIT2FIS) based classifier
and its projection based learning algorithm is presented in this paper. McIT2FIS consists of …
and its projection based learning algorithm is presented in this paper. McIT2FIS consists of …
Learning with auxiliary less-noisy labels
Y Duan, O Wu - IEEE transactions on neural networks and …, 2016 - ieeexplore.ieee.org
Obtaining a sufficient number of accurate labels to form a training set for learning a classifier
can be difficult due to the limited access to reliable label resources. Instead, in real-world …
can be difficult due to the limited access to reliable label resources. Instead, in real-world …
A metacognitive complex-valued interval type-2 fuzzy inference system
K Subramanian, R Savitha… - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
This paper presents a complex-valued interval type-2 neuro-fuzzy inference system
(CIT2FIS) and derive its metacognitive projection-based learning (PBL) algorithm …
(CIT2FIS) and derive its metacognitive projection-based learning (PBL) algorithm …
A metacognitive fully complex valued functional link network for solving real valued classification problems
M Sivachitra, S Vijayachitra - Applied Soft Computing, 2015 - Elsevier
In this paper, a sequential learning based meta-cognitive fully complex valued functional link
network (Mc-FCFLN) is developed for solving complex real world problems. Mc-FCFLN …
network (Mc-FCFLN) is developed for solving complex real world problems. Mc-FCFLN …
Classification of post operative breast cancer patient information using complex valued neural classifiers
M Sivachitra, S Vijayachitra - 2015 International Conference on …, 2015 - ieeexplore.ieee.org
Classification of Haberman's Survival information is useful to find out the patients survival
probability after a breast cancer surgery. Dataset has been collected from a standard …
probability after a breast cancer surgery. Dataset has been collected from a standard …
Planning and relaxed state EEG signal classification using complex valued neural classifier for brain computer interface
M Sivachitra, S Vijayachitra - 2015 International Conference on …, 2015 - ieeexplore.ieee.org
Most of the Brain Computer Interface (BCI) techniques use EEG signals as a main source.
Any BCI system consists of three modules and they are signal recorder, signal preprocessor …
Any BCI system consists of three modules and they are signal recorder, signal preprocessor …
Study of Complex-valued Learning algorithms for Post-surgery survival prediction
S Muthusamy, S Ramasamy - 2016 Second International …, 2016 - ieeexplore.ieee.org
Prediction of post-surgery survival of breast cancer patients is critical for long term medical
care. In this paper, we study the performances of several complex-valued classifiers in …
care. In this paper, we study the performances of several complex-valued classifiers in …