A new sequential learning algorithm using pseudo-Gaussian functions for neuro-fuzzy systems

2002 ◽  
Author(s):  
I. Rojas ◽  
H. Pomares ◽  
J. Gonzalez ◽  
P. Gloesekotter ◽  
J. Diestuhl ◽  
...  
Keyword(s):  
Fuzzy Systems ◽  
Learning Algorithm ◽  
Sequential Learning ◽  
Gaussian Functions ◽  
Neuro Fuzzy

HUMAN ACTION RECOGNITION USING META-COGNITIVE NEURO-FUZZY INFERENCE SYSTEM

2012 ◽  
Vol 22 (06) ◽  
pp. 1250028 ◽  
Author(s):  
K. SUBRAMANIAN ◽  
S. SURESH
Keyword(s):  
Action Recognition ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Human Action Recognition ◽  
Recognition System ◽  
Human Action ◽  
Sequential Learning ◽  
Inference System ◽  
Neuro Fuzzy

We propose a sequential Meta-Cognitive learning algorithm for Neuro-Fuzzy Inference System (McFIS) to efficiently recognize human actions from video sequence. Optical flow information between two consecutive image planes can represent actions hierarchically from local pixel level to global object level, and hence are used to describe the human action in McFIS classifier. McFIS classifier and its sequential learning algorithm is developed based on the principles of self-regulation observed in human meta-cognition. McFIS decides on what-to-learn, when-to-learn and how-to-learn based on the knowledge stored in the classifier and the information contained in the new training samples. The sequential learning algorithm of McFIS is controlled and monitored by the meta-cognitive components which uses class-specific, knowledge based criteria along with self-regulatory thresholds to decide on one of the following strategies: (i) Sample deletion (ii) Sample learning and (iii) Sample reserve. Performance of proposed McFIS based human action recognition system is evaluated using benchmark Weizmann and KTH video sequences. The simulation results are compared with well known SVM classifier and also with state-of-the-art action recognition results reported in the literature. The results clearly indicates McFIS action recognition system achieves better performances with minimal computational effort.

Hierarchical Neuro-Fuzzy Systems Part I

2011 ◽  
pp. 808-816
Author(s):  
Marley Vellasco ◽  
Marco Pacheco ◽  
Karla Figueiredo ◽  
Flavio Souza
Keyword(s):  
Fuzzy Systems ◽  
Knowledge Integration ◽  
Explicit Knowledge ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Neural Nets ◽  
Learning Capacity ◽  
Fuzzy Models ◽  
Neuro Fuzzy ◽  
Inference Systems

Neuro-fuzzy [Jang,1997][Abraham,2005] are hybrid systems that combine the learning capacity of neural nets [Haykin,1999] with the linguistic interpretation of fuzzy inference systems [Ross,2004]. These systems have been evaluated quite intensively in machine learning tasks. This is mainly due to a number of factors: the applicability of learning algorithms developed for neural nets; the possibility of promoting implicit and explicit knowledge integration; and the possibility of extracting knowledge in the form of fuzzy rules. Most of the well known neuro-fuzzy systems, however, present limitations regarding the number of inputs allowed or the limited (or nonexistent) form to create their own structure and rules [Nauck,1997][Nauck,19 98][Vuorimaa,1994][Zhang,1995]. This paper describes a new class of neuro-fuzzy models, called Hierarchical Neuro-Fuzzy BSP Systems (HNFB). These models employ the BSP partitioning (Binary Space Partitioning) of the input space [Chrysanthou,1996] and have been developed to bypass traditional drawbacks of neuro-fuzzy systems. This paper introduces the HNFB models based on supervised learning algorithm. These models were evaluated in many benchmark applications related to classification and time-series forecasting. A second paper, entitled Hierarchical Neuro-Fuzzy Systems Part II, focuses on hierarchical neuro-fuzzy models based on reinforcement learning algorithms.

NEURO-FUZZY SYSTEMS APPLIED ON A FIXED-WING AIRCRAFT CONTROL

2019 ◽  
Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto
Keyword(s):  
Fuzzy Systems ◽  
Aircraft Control ◽  
Neuro Fuzzy
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