A Robust Operator Functional State Fuzzy Modeling Approach Based on EEG Data

2014 ◽  
Vol 556-562 ◽  
pp. 4065-4068
Author(s):  
Shao Zeng Yang ◽  
Jian Hua Zhang
Keyword(s):  
Membership Function ◽  
Functional State ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Crossover Point ◽  
New Approach ◽  
Fuzzy Models ◽  
Eeg Data ◽  
Operator Functional State ◽  
Machine Systems

Operator functional state (OFS) is defined as the time-variable ability that an operator completes his/her assigned tasks. To evaluate the OFS in safety-critical human-machine systems, it is modeled by using the Wang-Mendel-based fuzzy system paradigm in this paper. The fuzzy model is constructed to correlate three EEG features (as model inputs) to the human-machine system performance (as model output). To derive a fuzzy model for real-time OFS assessment, the Gaussian membership function membership crossover point membership gradeδis found to be an essential parameter that controls the robustness of data-driven fuzzy models. The fuzzy models with differentδare applied to the OFS fuzzy modeling. The results have demonstrated that an appropriate value ofδcan be selected to derive robust fuzzy models. Compare with the results obtained by fuzzy models based on symmetric Gaussian membership functions, the new approach based on asymmetric Gaussian membership function leads to considerably improved robustness performance.

scholarly journals Operator functional state estimation based on EEG-data-driven fuzzy model

2016 ◽  
Vol 10 (5) ◽  
pp. 375-383 ◽  
Author(s):  
Jianhua Zhang ◽  
Zhong Yin ◽  
Shaozeng Yang ◽  
Rubin Wang
Keyword(s):  
State Estimation ◽  
Functional State ◽  
Fuzzy Model ◽  
Data Driven ◽  
Eeg Data ◽  
Operator Functional State

Uneven Input Space Division and Balance of Generality and Conciseness of Submodels for Hierarchical Fuzzy Modeling

2000 ◽  
Vol 4 (2) ◽  
pp. 152-157
Author(s):  
Kanta Tachibana ◽  
◽  
Takeshi Furuhashi ◽  
Keyword(s):  
Fuzzy Neural Network ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Performance Indices ◽  
Promising Technique ◽  
Input Space ◽  
Fuzzy Models ◽  
Multiple Input ◽  
Fuzzy Neural ◽  
Hierarchical Fuzzy Model

Hierarchical fuzzy modeling is a promising technique to describe input-output relationships of nonlinear systems with multiple input. This paper presents a new method of dividing input spaces for hierarchical fuzzy modeling using the Fuzzy Neural Network (FNN) and Genetic Algorithm (GA). Uneven division of input space for each submodel in the hierarchical fuzzy model can be achieved with the proposed method. The obtained hierarchical fuzzy models are likely more concise and more precise than those identified with conventional methods. Studies on effects of the weights on performance indices of generality and conciseness of the fuzzy model are also shown in this paper.

The Effects of Day-to-Day Variability of Physiological Data on Operator Functional State Classification

2013 ◽  
Author(s):  
James C. Christensen ◽  
Justin R. Estepp ◽  
Glenn F. Wilson ◽  
Christopher A. Russell ◽  
Krystal M. Thomas
Keyword(s):  
Functional State ◽  
Physiological Data ◽  
State Classification ◽  
Operator Functional State

Fuzzy Modeling to Simulate Surface Roughness in CNC Turning of AISI 1045 Steel

2011 ◽  
Vol 486 ◽  
pp. 262-265
Author(s):  
Amit Kohli ◽  
Mudit Sood ◽  
Anhad Singh Chawla
Keyword(s):  
Experimental Data ◽  
Surface Roughness ◽  
Process Parameters ◽  
Fuzzy Model ◽  
Fuzzy Modeling ◽  
Cnc Machine ◽  
Model Based ◽  
Aisi 1045 Steel ◽  
Aisi 1045 ◽  
1045 Steel

The objective of the present work is to simulate surface roughness in Computer Numerical Controlled (CNC) machine by Fuzzy Modeling of AISI 1045 Steel. To develop the fuzzy model; cutting depth, feed rate and speed are taken as input process parameters. The predicted results are compared with reliable set of experimental data for the validation of fuzzy model. Based upon reliable set of experimental data by Response Surface Methodology twenty fuzzy controlled rules using triangular membership function are constructed. By intelligent model based design and control of CNC process parameters, we can enhance the product quality, decrease the product cost and maintain the competitive position of steel.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

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