[BOOK][B] Complex-valued neural networks

A Hirose - 2006 - Wiley Online Library
Complex-valued neural networks Complex-Valued Neural Networks Page 2 IEEE Press 445
Hoes Lane Piscataway, NJ 08854 IEEE Press Editorial Board 2013 John Anderson, Editor in …

Minimax probability TSK fuzzy system classifier: A more transparent and highly interpretable classification model

Z Deng, L Cao, Y Jiang, S Wang - IEEE transactions on fuzzy …, 2014 - ieeexplore.ieee.org
When an intelligent model is used for medical diagnosis, it is desirable to have a high level
of interpretability and transparent model reliability for users. Compared with most of the …

A meta-cognitive learning algorithm for an extreme learning machine classifier

R Savitha, S Suresh, HJ Kim - Cognitive Computation, 2014 - Springer
This paper presents an efficient fast learning classifier based on the Nelson and Narens
model of human meta-cognition, namely 'Meta-cognitive Extreme Learning Machine …

A complex-valued neuro-fuzzy inference system and its learning mechanism

K Subramanian, R Savitha, S Suresh - Neurocomputing, 2014 - Elsevier
In this paper, we present a complex-valued neuro-fuzzy inference system (CNFIS) and
develop its meta-cognitive learning algorithm. CNFIS has four layers–an input layer with m …

Human action recognition using meta-cognitive neuro-fuzzy inference system

K Subramanian, S Suresh - International journal of neural systems, 2012 - World Scientific
We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference
System (McFIS) to efficiently recognize human actions from video sequence. Optical flow …

A meta-cognitive learning algorithm for a fully complex-valued relaxation network

R Savitha, S Suresh, N Sundararajan - Neural Networks, 2012 - Elsevier
This paper presents a meta-cognitive learning algorithm for a single hidden layer complex-
valued neural network called “Meta-cognitive Fully Complex-valued Relaxation Network …

Evolving and spiking connectionist systems for brain-inspired artificial intelligence

N Kasabov - Artificial intelligence in the age of neural networks and …, 2019 - Elsevier
Artificial neural networks have now a long history as major techniques in computational
intelligence with a wide range of application for learning from data and for artificial …

FROM MULTILAYER PERCEPTRONS AND NEUROFUZZY SYSTEMS TO DEEP LEARNING MACHINES: WHICH METHOD TO USE?-A SURVEY.

N Kasabov - … Journal on Information Technologies & Security, 2017 - search.ebscohost.com
Artificial neural networks have now a long history as major techniques in computational
intelligence with a wide range of application for learning from data. There are many methods …

Computer aided diagnosis of ADHD using brain magnetic resonance images

BS Mahanand, R Savitha, S Suresh - … , New Zealand, December 1-6, 2013 …, 2013 - Springer
This paper presents a pilot study on the development of an automated diagnostic tool for
Attention Deficiency Hyperactivity Disorder (ADHD) based on regional anatomy of the child …

Meta-cognitive neuro-fuzzy inference system for human emotion recognition

K Subramanian, S Suresh… - The 2012 International …, 2012 - ieeexplore.ieee.org
In this paper, we propose a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS) for
recognition of emotions from facial features. Local binary patterns have been proven to …