Optimization Of Fuzzy Models Research Articles

PENERAPAN LOGIKA FUZZY TSUKAMOTO BERBASIS WEB UNTUK KLASIFIKASI DAERAH RAWAN KECELAKAAN DI KOTA JAMBI

This study aims to apply web-based Tsukamoto fuzzy logic in the classification of accident areas in Jambi City. This method was developed by researchers at POLSERSTA Jambi city with the aim of increasing understanding of the risks of traffic accidents and helping make more effective decisions. Tsukamoto's fuzzy logic was chosen because of its ability to deal with uncertainty in the context of a classification involving various factors. The proposed system is able to take input data such as traffic levels, weather conditions, and road types, and convert them into a risk classification using fuzzy logic. The results of this system can provide guidance for traffic security officers and related agencies in managing potential accident areas. This research shows that web-based Tsukamoto fuzzy logic has the potential to be a useful tool in identifying traffic accident risk areas. However, improvements in data validation, optimization of fuzzy models, and collaboration with related parties need to be made to ensure the accuracy and usefulness of this system in improving traffic safety in Jambi City

A genetic reduction of feature space in the design of fuzzy models

The two fundamental conflicting requirements of fuzzy modeling, namely accuracy and transparency (interpretability) have resulted in a plethora of design methodologies and ensuing architectures of fuzzy models. This study contributes to the ongoing approaches to the enhancement of interpretability of fuzzy models by focusing on the reduction of input space in which such models are constructed. Fuzzy clustering realized through Fuzzy C-Means (FCM) is carried out in the input space of reduced dimensionality and the information granules obtained in such space are used to form a series of local models of the rule-based fuzzy model. The reduction of the input space is a problem of combinatorial nature and to solve this task, considered is the optimization scheme of genetic algorithms. In this sense, the feature (input) space reduction is realized by a wrapper algorithm, where the objective function guiding its development of the input space expresses the performance of the resulting fuzzy model. The design utilizes two commonly encountered concepts and functional constructs of fuzzy models, viz. information granules built in the reduced input space and a collection of local linear models forming the conclusion parts of the rules. Considering the well-established architecture of fuzzy models, the main objective of this study is to present a way of an efficient reduction of the input space leading to the enhanced interpretability of the fuzzy models and investigate and quantify possibilities of optimization of fuzzy models with regard. The underlying design issues being of particular practical interest in this modeling context deal with the dependencies between the level of reduction of the input space versus the granularity of the fuzzy model (viz. the number of its rules) and a quantification of an impact of the parameters of the granules (fuzzification coefficient) on the performance of the obtained model. The experimental studies highlighting the numerical aspects of the design comprise experimentation with synthetic data and those publicly available at several data sites.

Optimization of Fuzzy Models using a Novel PSO Algorithm: Application to Medical Databases

AbstractThis paper proposes the application of a new wrapper particle swarm optimization (PSO) method to feature selection problems. The new method was designed to cope with premature convergence for the binary PSO algorithm. Its principal objective is to improve classification accuracy of fuzzy models. Further, the method is tested in a medical database, namely in the prediction of the outcome (survived or deceased) of sepsis patients.

Optimization of fuzzy model using genetic algorithm for process control application

A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This paper discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach.

COMPETITIVE LEARNING OF FUZZY MODELS

In general, fuzzy modeling requires two stages: structure identification (generating the fuzzy rule base) and parameter learning (optimizing parameters in fuzzy rules). Here, we present an on-line algorithm for competitive learning and optimization of fuzzy models. Differing from existing methods, in this approach the structure identification and parameter optimization of the fuzzy model can be carried out automatically, using on-line acquisition of data. We demonstrate this approach by applying it to different types of nonlinear system modeling.

An algorithmic framework for development and optimization of fuzzy models

Fuzzy models can be conveniently regarded as linguistic modelling structures with well-defined functional blocks of input and output interfaces along with a processing module. This paper examines the functions of these modules and specifies the relevant optimization tasks which accrue to them. Considering several levels of memorization completed within fuzzy models (resulting in establishing short-, medium- and long-term memories), the corresponding learning policies are developed. Some classes of optimization mechanisms are also discussed, with particular emphasis focused on their ability to handle multimodal problems. These tools comprise both gradient-based techniques and selected methods of evolutionary optimization, in particular genetic algorithms. Illustrative numerical studies are also included.

Optimization of fuzzy models

Fuzzy models are regarded as linguistic modeling structures with well-defined functional blocks of input and output interfaces along with a processing module. The paper examines the functions of these modules and specifies the relevant optimization tasks emerging in fuzzy system identification. Considering several distinct levels of conceptual memorization realized within the fuzzy models (subsequently resulting in establishing short-, medium-, and long-term memories), the corresponding learning policies are developed. The study includes also detailed simulation studies.