FORMATION OF FUZZY IF-THEN RULES AND MEMBERSHIP FUNCTION USING ENHANCED PARTICLE SWARM OPTIMIZATION

2013 ◽  
Vol 21 (01) ◽  
pp. 103-126 ◽  
Author(s):  
P. GANESHKUMAR ◽  
C. RANI ◽  
S. N. DEEPA
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Particle Swarm ◽  
Bench Mark ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Rule Set ◽  
Enhanced Particle Swarm Optimization ◽  
Test Result

This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and Non-uniform mutation from Genetic Algorithm (GA) are incorporated in addition to standard velocity and position updating of PSO. The performance of the proposed approach is evaluated using ten publicly available bench mark data sets. From the simulation study, it is found that the proposed approach enhances the convergence and generates a comprehensible fuzzy classifier system with high classification accuracy for all the data sets. Statistical analysis of the test result shows the suitability of the proposed method over other approaches reported in the literature.

Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 108-122 ◽  
Author(s):  
Halima Salah ◽  
Mohamed Nemissi ◽  
Hamid Seridi ◽  
Herman Akdag
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Particle Swarm ◽  
Main Idea ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.

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

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

AUTOMATIC DEVELOPMENT OF FUZZY MEMBERSHIP FUNCTIONS ON HEPATITIS PATIENTS DATA USING PARTICLE SWARM OPTIMIZATION (PSO)

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Candra Dewi ◽  
Ratna Putri P.S ◽  
Indriati Indriati
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Fuzzy System ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Membership Functions ◽  
Swarm Optimization ◽  
Global Weight ◽  
The Status ◽  
And Function

Information about the status of disease (prognosis) for patients with hepatitis is important to determine the type of action to stabilize and cure this disease. Among some system, fuzzy system is one of the methods that can be used to obtain this prognosis. In the fuzzification process, the determination of the exact range of membership function will influence the calculation of membership degree and of course will affect the final value of fuzzy system. This range and function can usually be formed using intuition or by using an algorithm. In this paper, Particle Swarm Optimization (PSO) algorithm is implemented to form the triangular membership functions in the case of patients with hepatitis. For testing process, this paper conducts four scenarios to find the best combination of PSO parameter values . Based on the testing it was found that the best parameters to form a membership function range for the hepatitis data is about 0.9, 0.1, 2, 2, 100, 500 for inertia max, inertia min, local ballast constant, global weight constant, the number of particles, and maximum iterations respectively.  

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.

Sign in / Sign up

Export Citation Format

Share Document