Fuzzy Logic Controllers are knowledge-based systems, incorporating human knowledge into their Knowledge Base through Fuzzy Rules and Fuzzy Membership Functions (among other information elements). The deenition of these Fuzzy Rules and Fuzzy Membership Functions is actually aaected by subjective decisions, having a great innuence over the performance of the Fuzzy Controller. This paper describes...

This paper presents a fuzzy system approach to the prediction of nonlinear time series and dynamical systems based on a fuzzy model that includes its derivative information. The underlying mechanism governing the time series, expressed as a set of IF–THEN rules, is discovered by a modified structure of fuzzy system in order to capture the temporal series and its temporal derivative information....

fuzzy rules based classifier systems (FRBS) have gained more popularity in recent years. This classifier suffers from the problem of pattern space partitioning, to reach a compact set of rules that provides high classification power. In this paper, we propose an adaptive hierarchical fuzzy partitioning method based on tree decomposition. This decomposition is controlled by the grade of certaint...

The thesis deals with fuzzy computing in control systems and particularly in multivariable control and gain scheduling. It addresses research problems as to how to apply methods of fuzzy set theory efficiently in control tasks, and how to tune a fuzzy controller. The problem is solved by analyzing properties of fuzzy sets and fuzzy systems, selecting a structure for the fuzzy system, and packag...

We propose a novel scheme for designing fuzzy rule based classifiers for gene expression data analysis. A neural network based method is used for selecting a set of informative genes. Considering only these selected set of genes, we cluster the expression data with a fuzzy clustering algorithm. Each cluster is then converted into a fuzzy if-then rule, which models an area in the input space. Th...

This paper presents a fuzzy genetic approach to perform data classification. The method aims to obtain small fuzzy classifiers by means of optimization of fuzzy rules bases using a genetic algorithm. It is shown how a fuzzy rules base is generated from a numerical database and how its best subset is found by the genetic algorithm. The classifiers are evaluated in terms of accuracy, cardinality ...

This paper presents an approach to approximate reasoning over a set of IF-THEN rules called fuzzy logic deduction. It understands IF-THEN rules as linguistically expressed logical implications and interprets them inside formal logical theory. Methodology and some properties

Fuzzy rule based systems have been very popular in many engineering applications. In petroleum engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the petrophysical properties prediction model. However, when generating fizzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input varia...

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy – Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is the base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into several di...