scholarly journals Optimizing Fuzzy Rule Base for Illumination Compensation in Face Recognition using Genetic Algorithms

2014 ◽  
Vol 2 (2) ◽  
Author(s):  
Bima Sena Bayu Dewantara ◽  
Jun Miura
Keyword(s):  
Genetic Algorithm ◽  
Face Recognition ◽  
High Performance ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Experimental Result ◽  
Rule Base ◽  
Negative Effects ◽  
Reflectance Model ◽  
Fuzzy Genetic

Fuzzy rule optimization is a challenging step in the development of a fuzzy model. A simple two inputs fuzzy model may have thousands of combination of fuzzy rules when it deals with large number of input variations. Intuitively and trial‐error determination of fuzzy rule is very difficult. This paper addresses the problem of optimizing Fuzzy rule using Genetic Algorithm to compensate illumination effect in face recognition. Since uneven illumination contributes negative effects to the performance of face recognition, those effects must be compensated. We have developed a novel algorithmbased on a reflectance model to compensate the effect of illumination for human face recognition. We build a pair of model from a single image and reason those modelsusing Fuzzy.Fuzzy rule, then, is optimized using Genetic Algorithm. This approachspendsless computation cost by still keepinga high performance. Based on the experimental result, we can show that our algorithm is feasiblefor recognizing desired person under variable lighting conditions with faster computation time.Keywords: Face recognition, harsh illumination, reflectance model, fuzzy, genetic algorithm

Provide A Method Based on Genetic Algorithm to Optimize the Fuzzy Logic Controller for the Inverted Pendulum

2021 ◽  
Author(s):  
Shahrooz Alimoradpour ◽  
Mahnaz Rafie ◽  
Bahareh Ahmadzadeh
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy System ◽  
Inverted Pendulum ◽  
Fuzzy Logic Controller ◽  
Fuzzy Controller ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Base System ◽  
Control Rules ◽  
Dynamics And Control

Abstract One of the classic systems in dynamics and control is the inverted pendulum, which is known as one of the topics in control engineering due to its properties such as nonlinearity and inherent instability. Different approaches are available to facilitate and automate the design of fuzzy control rules and their associated membership functions. Recently, different approaches have been developed to find the optimal fuzzy rule base system using genetic algorithm. The purpose of the proposed method is to set fuzzy rules and their membership function and the length of the learning process based on the use of a genetic algorithm. The results of the proposed method show that applying the integration of a genetic algorithm along with Mamdani fuzzy system can provide a suitable fuzzy controller to solve the problem of inverse pendulum control. The proposed method shows higher equilibrium speed and equilibrium quality compared to static fuzzy controllers without optimization. Using a fuzzy system in a dynamic inverted pendulum environment has better results compared to definite systems, and in addition, the optimization of the control parameters increases the quality of this model even beyond the simple case.

Optimized Fuzzy Logic Control Strategy for Parallel Hybrid Electric Vehicle Based on Genetic Algorithm

2013 ◽  
Vol 274 ◽  
pp. 345-349 ◽  
Author(s):  
Mei Lan Zhou ◽  
Deng Ke Lu ◽  
Wei Min Li ◽  
Hui Feng Xu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Fuzzy Logic Control ◽  
Hybrid Electric Vehicle ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Membership Functions ◽  
Logic Control

For PHEV energy management, in this paper the author proposed an EMS is that based on the optimization of fuzzy logic control strategy. Because the membership functions of FLC and fuzzy rule base were obtained by the experience of experts or by designers through the experiment analysis, they could not make the FLC get the optimization results. Therefore, the author used genetic algorithm to optimize the membership functions of the FLC to further improve the vehicle performance. Finally, simulated and analyzed by using the electric vehicle software ADVISOR, the results indicated that the proposed strategy could easily control the engine and motor, ensured the balance between battery charge and discharge and as compared with electric assist control strategy, fuel consumption and exhaust emissions have also been reduced to less than 43.84%.

Implementing Fuzzy Learning Algorithms in a 6 DOF Hydraulic Parallel Link Manipulator: Actuators' Fuzzy Modeling

2002 ◽  
Vol 14 (4) ◽  
pp. 408-419 ◽  
Author(s):  
Zakarya Zyada ◽  
◽  
Yasuhisa Hasegawa ◽  
Gancho Vachkov ◽  
Toshio Fukuda
Keyword(s):  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Hydraulic Actuator ◽  
Fuzzy Rule Base ◽  
Learning Rules ◽  
Model Rule ◽  
Parallel Link ◽  
Fuzzy Learning ◽  
Good Agreement

A fuzzy-logic-based model, suitable for force control, for each hydraulic actuator of a parallel link manipulator is presented. Constructing the fuzzy model rule base mainly consists of 2 stages: (1) learning rules from examples for the known acquired input/output data of the hydraulic actuators and (2) completing unknown fuzzy rules from heuristics and experience based on the logic of actuators' behavior. We first present the algorithm of fuzzy-rule base modeling and its application for one actuator. We then present fuzzy rule base results characterizing each hydraulic actuator, differing from one to another, of a 6 DOF parallel link manipulator. Simulation output results from fuzzy models show good agreement with experimental results.

An Application of T-S Model and Phase Based Quantum Genetic Algorithm in Oilfield

2014 ◽  
Vol 513-517 ◽  
pp. 1392-1397
Author(s):  
Shu Xia Liu ◽  
Yong Yang ◽  
Dian Bao Mu ◽  
Pan Chi Li
Keyword(s):  
Genetic Algorithm ◽  
Prediction Model ◽  
Influencing Factors ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Quantum Genetic Algorithm ◽  
Fuzzy Rule Base ◽  
Water Cut ◽  
S Model ◽  
Oilfield Development

Based on the learning and integrated application of the T-S modeling method and Phase based Quantum Genetic Algorithm (PQGA), this article aims to provide a new and effective method to fulfill the actual demand of the oilfield development and production. First, according to the forecast indicators and the influencing factors, establish the fuzzy rule base, then according to the fuzzy rule base, establish the T-S prediction model, with improved quantum genetic algorithm to optimize the parameters of the T-S model, through the application of the prediction of the water-cut in oilfield, we prove that the method is effective.

A Genetic Algorithm Approach for Discovering Tuned Fuzzy Classification Rules with Intra- and Inter-Class Exceptions

2016 ◽  
Vol 25 (2) ◽  
pp. 263-282 ◽  
Author(s):  
Renu Bala ◽  
Saroj Ratnoo
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Rule Base ◽  
Second Phase ◽  
Classification Rules ◽  
Linguistic Terms ◽  
Data Set ◽  
Actual Distribution ◽  
Genetic Algorithm Approach

AbstractFuzzy rule-based systems (FRBSs) are proficient in dealing with cognitive uncertainties like vagueness and ambiguity imperative to real-world decision-making situations. Fuzzy classification rules (FCRs) based on fuzzy logic provide a framework for a flexible human-like reasoning involving linguistic variables. Appropriate membership functions (MFs) and suitable number of linguistic terms – according to actual distribution of data – are useful to strengthen the knowledge base (rule base [RB]+ data base [DB]) of FRBSs. An RB is expected to be accurate and interpretable, and a DB must contain appropriate fuzzy constructs (type of MFs, number of linguistic terms, and positioning of parameters of MFs) for the success of any FRBS. Moreover, it would be fascinating to know how a system behaves in some rare/exceptional circumstances and what action ought to be taken in situations where generalized rules cease to work. In this article, we propose a three-phased approach for discovery of FCRs augmented with intra- and inter-class exceptions. A pre-processing algorithm is suggested to tune DB in terms of the MFs and number of linguistic terms for each attribute of a data set in the first phase. The second phase discovers FCRs employing a genetic algorithm approach. Subsequently, intra- and inter-class exceptions are incorporated in the rules in the third phase. The proposed approach is illustrated on an example data set and further validated on six UCI machine learning repository data sets. The results show that the approach has been able to discover more accurate, interpretable, and interesting rules. The rules with intra-class exceptions tell us about the unique objects of a category, and rules with inter-class exceptions enable us to take a right decision in the exceptional circumstances.

scholarly journals Software Quality Prediction Method Using Fuzzy Logic

2021 ◽  
Vol 12 (11) ◽  
pp. 807-817
Author(s):  
Ritu, Et. al.
Keyword(s):  
Fuzzy Logic ◽  
Software Quality ◽  
Reference Model ◽  
Fuzzy Model ◽  
Prediction Method ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Development Organization ◽  
Soft Computing Techniques

: Software Quality is the key priority of today’s marketplace and software development organization to which a system, technique, or factor meets particular requirements and conditions. Soft computing techniques play a vital role in developing software engineering applications. In this paper, we have identified five parameters: Reliability, Efficiency, Usability, Maintainability, and Portability for accessing the level of quality of software. A fuzzy logic-based intelligent identification methodology has been proposed to access the quality of particular software-based on five parameters. The proposed identification scheme takes these five parameters as input and predicts the quality of the software using the fuzzy rule base which is generated using various studies. As this scheme takes five inputs and each input is divided into three regions i.e. ‘Low’, ‘Medium’, ‘High’ and thus a total of 35 i.e. 243 rules has been generated to analyze the software quality. Furthermore, Mamdani fuzzy model has been used as the reference model. To show the effectiveness of the proposed methodology, simulation results have been performed in MATLAB, which shows that the software's quality closely matches with the actual one.

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