Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface

It is difficult to measure something we cannot clearly define. No wonder that, for the over 100 definitions of the creativity proposed in the literature, there are almost as many scales and assessment tools. Most of these instruments have been designed for research purposes, and are difficult to apply and score, especially in the educational environment. Not to mention that they are expensive. The research described in this paper is aimed to develop a free, fast, and easy to use software tool for the assessment of creativity in the educational context. To this purpose, we have designed a new scale with 20 items, based on a novel approach focusing on detecting the factors known to block the creativity, like stereotypical thinking, and social conformity. The user input is collected through a web based interface, and the actual interpretation of the results is automated by means of a fuzzy logic algorithm. The proposed solution is interesting because it can be easily integrated in almost any e-learning platform, or used as a stand-alone tool for tracing the evolution of the students involved in courses for the development of creative thinking skills, and also for possible other applications.

Section: Divergent Thinking Convergent Thinking S U B J E C T Iv It Ymentioning

confidence: 99%

A Fuzzy Logic Software Instrument and a New Scale for the Assessment of Creativity

Șușnea

Vasiliu

2016

“…We can enumerate some contributions quoted in this work: Alina Alb-Lupas [2], Razvan Andonie [3], Marius Balas [6][7][8], Valentina E. Balas [6][7][8]58], Adrian I. Ban [9], Boldur E. Barbat [38], Barnabas Bede [11,12], Lucian Coroianu [9], Otilia Dragomir [30], Ioan Dzitac [22,[36][37][38][39][40][41]73,88,107,118,119,145], Simona Dzitac [22,[39][40][41]118,119], Ioan Felea [39][40][41]118], Sorin G. Gal [12], George Georgescu [44], Vasile Lupulescu [59], Dorel Mihet [65], Sorin Nadaban [22,[71][72][73][74], Georgia I. Oros [86,…”

Section: Lotfi a Zadehmentioning

confidence: 99%

Fuzzy Logic Is Not Fuzzy: World-renowned Computer Scientist Lotfi A. Zadeh

Dzițac¹,

Filip²,

Manolescu³

2017

Self Cite

115

“…Tomescu, S. Preitl, E.M. Petriu, M.-B. Rădac membership functions of these linguistic terms are: [4,8] for LT x 3 ,1 , [5,9] for LT x 3 ,2 , for [6,10], LT x 3 ,3 , [7,11] for LT x 3 ,4 , and [8,12] for LT x 3 ,5 . The expressions of the trapezoidal membership functions are:…”

Section: Approach To Takagi-sugeno Fuzzy Modelingmentioning

confidence: 99%

“…Several approaches to fuzzy modeling of nonlinear servo systems are given in the literature. They belong to the general framework of nonlinear process models [1], [2], [3], [4], [5]. A parallel distributed compensation scheme is proposed in [6] with focus on fuzzy reference models; the linear matrix inequalities are formulated and solved in order to linearize the errors between the feedback system and the nonlinear reference model.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Fuzzy Modeling of Electromagnetic Actuated Clutch Systems

Claudia-Adina

Precup

Marius

et al. 2013

This paper proposes an approach to fuzzy modeling of a nonlinear servo system application represented by an electromagnetic actuated clutch system. The nonlinear model of the process is simplified and linearized around several operating points of the input-output static map of the process. Discrete-time Takagi-Sugeno (T-S) fuzzy models of the processes are derived on the basis of the modal equivalence principle; the rule consequents of these T-S fuzzy models contain the state-space models of the process. Three discrete-time T-S fuzzy models are suggested, compared and validated by simulation results.