GENEFIS: Toward an Effective Localist Network

Pratama, Mahardhika; Anavatti, Sreenatha G.; Lughofer, Edwin

doi:10.1109/tfuzz.2013.2264938

Cited by 144 publications

(106 citation statements)

References 45 publications

Supporting

Mentioning

106

Contrasting

Order By: Relevance

“…The use of evolving classifiers for activity recognition from sensor readings in ambient assisted living environments is described by [27]. In [30] and [31], two novel evolving neurofuzzy algorithms are developed. A new concept is addressed in [41] for handling drifts in data streams during the run of online evolving modeling processes in a regression context.…”

Section: Introductionmentioning

confidence: 99%

MSAFIS: an evolving fuzzy inference system

Rubio

Bouchachia

2015

Soft Comput

View full text Add to dashboard Cite

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), uniform stable backpropagation algorithm (SBP). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SBP is used instead of the Kalman filter for the updating of parameters. The SBP has the advantage that is obtains a better learning than the Kalman filter in big data. The effectiveness of the studied methods is verified by two experiments.

show abstract

Section: Introductionmentioning

confidence: 99%

MSAFIS: an evolving fuzzy inference system

Rubio

Bouchachia

2015

Soft Comput

View full text Add to dashboard Cite

show abstract

“…Among them we have, for example, the perceptron [10], the multilayer feedforward network [11], the probabilistic network [12]. Interesting alternative based on fuzzy logic can be found in [13], [14]. In this work, we focus on multilayer feedforward network, which consist of many neurons, each of them fully connected to every neuron in adjacent forward layers, although the technique is not limited to this particular implementation.…”

Section: Formulation and Methodologymentioning

confidence: 99%

On randomization of neural networks as a form of post-learning strategy

Kapanova¹,

Dimov²,

Sellier³

2015

Soft Comput

View full text Add to dashboard Cite

Today artificial neural networks are applied in various fields -engineering, data analysis, robotics. While they represent a successful tool for a variety of relevant applications, mathematically speaking they are still far from being conclusive. In particular, they suffer from being unable to find the best configuration possible during the training process (local minimum problem). In this paper, we focus on this issue and suggest a simple, but effective, post-learning strategy to allow the search for improved set of weights at a relatively small extra computational cost. Therefore, we introduce a novel technique based on analogy with quantum effects occurring in nature as a way to improve (and sometimes overcome) this problem. Several numerical experiments are presented to validate the approach.

show abstract

“…Evolve [9] uses generalized fuzzy rules in arbitrarily rotated position for increased precision, which was then re-used on regression problems [45] Another simplified alternative to define the antecedent part of fuzzy evolving systems has been presented, using data Clouds and density distribution [46]. GENEFIS [47] delivers a sensible trade-off between high predictive accuracy and parsimonious rule base while reckoning tractable rule.…”

Section: Related Workmentioning

confidence: 99%

Online active supervision of an evolving classifier for customized-gesture-command learning

Bouillon

Anquetil

2017

Neurocomputing

View full text Add to dashboard Cite

To cite this version:Manuel Bouillon, Eric Anquetil.Online active supervision of an evolving classifier for customized-gesture-command learning.Neurocomputing AbstractTouch sensitive interfaces enable new interaction methods, like gesture commands. For users to easily memorize more than a dozen of gesture commands, it is important to enable gesture set customization. The classifier used to recognize drawn symbols must hence be customizable -able to learn from very few data -and evolving -able to learn new classes on-the-fly and improve during its use. The objective of this work is to obtain a gesture command system that cooperates as best as possible with the user: that learns from its mistakes without soliciting the user too often. This paper presents a novel approach for the online active learning of gesture commands, with three contributions. The IntuiSup supervisor monitors the learning process and user interactions. The Evolving Sampling by Uncertainty (ESU) algorithm enables to maintain the error/interaction compromise over time. The Boosted-ESU (B-ESU) method optimizes interaction impact to fasten system learning speed. The efficiency of our approach is evaluated on the publicly available ILG Data Base of gesture commands. Experimentation shows the effectiveness of the supervision strategy and improvements both in term of accuracy and learning speed.

show abstract

GENEFIS: Toward an Effective Localist Network

Cited by 144 publications

References 45 publications

MSAFIS: an evolving fuzzy inference system

MSAFIS: an evolving fuzzy inference system

On randomization of neural networks as a form of post-learning strategy

Online active supervision of an evolving classifier for customized-gesture-command learning

Contact Info

Product

Resources

About