In this paper, we describe a new application domain for intelligent autonomous systems – Intelligent Buildings (IB). In doing so we present a novel approach to the implementation of IB agents based on a hierarchical fuzzy genetic multi embedded-agent architecture comprising a low-level behaviour based reactive layer whose outputs are co-ordinated in a fuzzy way according to deliberative plans. ...

Fuzzy rule based systems are increasingly being used to deal with time series processes that may lack stochastic stability due to non-stationarity, multiscaling and persistent autocorrelations. Wavelet filtering can be used to deal with such phenomenon. A method for creating a fuzzy-rule base from a time series, where the first difference (returns) of the preprocessed series is used, and high f...

In this paper, the problem of determining the quality level, lead time for order delivery and price of a product produced by a manufacturer is considered. In this problem the demand for the product is influenced by all three decision variables: price, lead time and quality level. To formulate the demand function, a fuzzy rule base that estimates the demand value based on the three decision vari...

Data projection is an important tool in exploratory data analysis. Sammon’s non linear projection method lacks predictability and is ineffective for large data sets. To introduce predictability we implement an extension of Sammon’s algorithm using fuzzy logic approach. The fuzzy based rule model is implemented in the .Net framework using Microsoft Visual Studio with Visual C# as the programming...

SLAVE is an inductive learning algorithm that uses concepts based on fuzzy logic theory. This theory has been shown to be a useful representational tool for improving the understanding of the knowledge obtained from a human point of view. Furthermore, SLAVE uses an iterative approach for learning based on the use of a genetic algorithm (GA) as a search algorithm. In this paper, we propose a mod...

we study the problem of reconstructing binary images from four projections data in a fuzzy environment. given the uncertainly projections,w e want to find a binary image that respects as best as possible these projections. we provide an iterative algorithm based on fuzzy integer programming and linear membership functions.

One of the possible semantics of fuzzy sets is in terms of similarity, namely a grade of membership of an item in a fuzzy set can be viewed as the degree of resemblance between this item and prototypes of the fuzzy set. In such a framework, an interesting question is how to devise a logic of similarity, where inference rules can account for the proximity between interpretations. The aim is to c...