Immune Algorithm for Optimization of Membership Function in Fuzzy Models

2015 ◽  
pp. 157-167
Author(s):  
Bogumiła Mrozek
Keyword(s):  
Membership Function ◽  
Immune Algorithm ◽  
Fuzzy Models

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.

Research on Classification Effectiveness of the Novel Mamdani Fuzzy Classifier

2014 ◽  
Vol 511-512 ◽  
pp. 871-874
Author(s):  
Hong Yan Zuo ◽  
Zhou Quan Luo ◽  
Chao Wu
Keyword(s):  
Membership Function ◽  
Prediction Accuracy ◽  
Simulation Experiment ◽  
Fitness Function ◽  
Immune Algorithm ◽  
Fuzzy Classification ◽  
Data Sets ◽  
The Novel ◽  
Fuzzy Classifier

A novel Mamdani fuzzy classifier based on improved chaos immune algorithm is developed, in which bilateral Gaussian membership function parameters are set as constraint conditions and the indexes of fuzzy classification effectiveness and number of correct samples of fuzzy classification as the subgoal of fitness function. Moreover, Iris database is used for classification effectiveness simulation experiment. The results show that Mamdani fuzzy classifier based on improved chaos immune algorithm can effectively improve the prediction accuracy of classification of data sets with noises and outliers.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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