Design Of Fuzzy Rule-based Systems Research Articles

ANALYSIS OF FUZZY IMPLICATION

Fuzzy logic (fuzzy sets) possesses some widespread application in decision-making due to its ability to handle uncertainties in the implemented data and information. The application of fuzzy logic in decision-making usually involves the design of fuzzy rule-based systems. The functioning of the designed fuzzy systems can be compactly defined as the fuzzy inference process. One of the main steps of fuzzy inference is fuzzy implication, and, therefore, the selection of proper implication function possesses the great impact on the corresponding decision-making process. The currently reported research provides a comparative analysis on two fuzzy implications: Ali-2 and Zadeh. The reported research involves the application of an efficiency index and is performed on a real-world example. Keywords: Fuzzy implication, If-Then rules, membership function, fuzzy set, efficiency index

Design Of Fuzzy Rule-Based System Using Genetic Algorithms

The aim of this paper is to obtain a compact and optimal fuzzy rule-based model from observation data by utilizing the Genetic algorithm technique. The approach is optimized by applying Genetic Algorithms, owing to its capability of searching irregular and high dimensional solution spaces. Genetic Algorithms has been applied to learn consequent part of fuzzy rules and models with fixed number of rules. In the work we propose a Genetic algorithm approach to a non-linear air conditioning system for the construction of optimal fuzzy rules in two steps. First, fuzzy clustering is applied to obtain an initial rule based model having pre-calculated number of rules with antecedents only. In the second step, the regions of rule-consequents are obtained by a binary coded Genetic Algorithm which leads to the extraction of an optimal rule based model.

Handling fuzzy systems’ accuracy-interpretability trade-off by means of multi-objective evolutionary optimization methods – selected problems

Abstract The paper addresses several open problems regarding the automatic design of fuzzy rule-based systems (FRBSs) from data using multi-objective evolutionary optimization algorithms (MOEOAs). In particular, we propose: a) new complexity-related interpretability measure, b) efficient strong-fuzzy-partition implementation for improving semantics-related interpretability, c) special-coding-free implementation of rule base and original genetic operators for its processing, and d) implementation of our ideas in the context of well-known MOEOAs such as SPEA2 and NSGA-II. The experiments demonstrate that our approach is an effective tool for handling FRBSs’ accuracy-interpretability trade-off, i.e, designing FRBSs characterized by various levels of such a trade-off (in particular, for designing highly interpretability-oriented systems of still competitive accuracy).

Development of Intelligent Wireless Sensor Networks for Human Comfort Index Measurement

Abstract Conventional wireless home automation networks (WHANs) incorporate embedded wireless sensors and actuators that monitors and control home living environment. WHANs primary goal is to maintain user comfort and efficient home management. Conventional WHAN lacks higher “intelligence” in term of managing compound human comfort where as it deals with multitude of human comfort factors individually instead of collectively. This paper presents wireless sensor networks (WSN) based Human Comfort Ambient Intelligence (HCAmI) system. The design of a Fuzzy Rule Based System (FRBS) for the measurement of Human Comfort Index (HCI) in a living space is presented. The design of FRBS is described and the system is evaluated and tested by using combined simulated and empirical data. It explores the complex relationship between multiple comfort factors in human comfort provisioning. The comfort factors considered here include thermal comfort, visual comfort, indoor air comfort and acoustical comfort.

Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing

In this contribution, we will analyse the importance of the fuzzy partition granularity for the linguistic variables in the design of fuzzy rule-based systems (FRBSs). In order to put this into effect, we will study the FRBS behaviour considering uniform fuzzy partitions with the same number of labels for all the linguistic variables, and considering uniform fuzzy partitions with any number of labels for each linguistic variable. We will present a method based on Simulated Annealing (SA) in order to obtain a good uniform fuzzy partition granularity that improves the FRBS behaviour. It is an efficient granularity search method for finding a good number of labels per variable.