In heterogeneous environments with performance variations present, multiple applications compete and share a limited amount of system resources, and su#er from variations in resource availability. These complex applications are desired to adapt themselves and to adjust their resource demands dynamically. On one hand, current adaptation mechanisms built within an application cannot preserve global properties such as fairness; on the other hand, adaptive resource management mechanisms built within the operating system are not aware of data semantics in the application. In this paper, we present a novel Middleware Control Framework to enhance the e#ectiveness of QoS adaptation decisions by dynamic control and reconfiguration of internal parameters and functionalities of a distributed multimedia application. Our objective is to satisfy both system-wide properties (such as fairness among concurrent applications) and application-specific requirements (such as preserving the critical performance criteria). The framework is modeled by the Task Control Model and the Fuzzy Control Model, based on rigorous results from the control theory, and verified by the controllability and adaptivity of a distributed visual tracking application. The results show validation of the framework, i.e., critical application quality parameter can be preserved via controlled adaptation.

The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable Genetic Algorithms. Keywords: Fuzzy logic control systems, learning, genetic algorithms. 1 Introduction Fuzzy rule based systems have been shown to be an important tool for modelling complex systems, in which due to the complexity or the imprecision, classical tools are unsuccessful. Fuzzy Logic Controllers (FLCs) are now considered as one of the most important applications of the fuzzy rule based systems. The e...

Although fuzzy systems demonstrated their ability to solve different kinds of problems in various applications, there is an increasing interest on augmenting them with learning capabilities. Two of the most successful approaches to hybridise fuzzy systems with adaptation methods have been made in the realm of soft computing: neuro-fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. This contribution focus on genetic fuzzy systems, paying special attention to genetic fuzzy rule based systems, giving a brief overview of the field.

Real-world electrical engineering problems can take advantage of the last Data Analysis methodologies. In this paper we will show that Genetic Fuzzy Rule-Based Systems and Genetic Programming techniques are good choices for tackling with some practical modeling problems. We claim that both evolutionary processes may produce good numerical results while providing us with a model that can be interpreted by a human being. We will analyze in detail the characteristics of these two methods and we will compare them to the some of the most popular classical statistical modeling methods and neural networks.

Fuzzy rules have been advocated as a key tool for expressing pieces of knowledge in "fuzzy logic". However, there does not exist a unique kind of fuzzy rules, nor is there only one type of "fuzzy logic". This diversity has caused many a misunderstanding in the literature of fuzzy control. The paper is a survey of different possible semantics for a fuzzy rule and shows how they can be captured in the framework of fuzzy set and possibility theory. It is pointed out that the interpretation of fuzzy rules dictates the way the fuzzy rules should be combined. The various kinds of fuzzy rules considered in the paper (gradual rules, certainty rules, possibility rules, and others) have different inference behaviors and correspond to various intended uses and applications. The representation of fuzzy unless-rules is briefly investigated on the basis of their intended meaning. The problem of defining and checking the coherence of a block of parallel fuzzy rules is also briefly addressed. This iss...

This paper examines the effect of rule weights in fuzzy rule-based classification systems. Each fuzzy if-then rule in our classification system has antecedent linguistic values and a single consequent class. We use a fuzzy reasoning method based on a single winner rule in the classification phase. The winner rule for a new pattern is the fuzzy if-then rule that has the maximum compatibility grade with the new pattern. When we use fuzzy if-then rules with certainty grades (i.e., rule weights), the winner is determined as the rule with the maximum product of the compatibility grade and the certainty grade. In this paper, the effect of rule weights is illustrated by drawing classification boundaries using fuzzy if-then rules with/without certainty grades. It is also shown that certainty grades play an important role when a fuzzy rule-based classification system is a mixture of general rules and specific rules. Through computer simulations, we show that comprehensible fuzzy rule-based systems with high classification performance can be designed without modifying the membership functions of antecedent linguistic values when we use fuzzy if-then rules with certainty grades.

Fuzzy Logic Controllers constitute knowledge-based systems that include Fuzzy Rules and Fuzzy Membership Functions to incorporate human knowledge into their knowledge base. The definition of fuzzy rules and fuzzy membership functions is one of the key question when designing Fuzzy Logic Controllers, and is generally affected by subjective decisions. Some efforts have been made to obtain an improvement on system performance by incorporating learning mechanisms to modify the rules and/or membership functions of the FLC. Genetic Algorithms are probabilistic search and optimization procedures based on natural genetics. This paper proposes a way to apply (with a learning purpose) Genetic Algorithms to Fuzzy Logic Controllers, and presents an application designed to control the Synthesis of biped walk of a simulated 2-D biped robot. KEYWORDS: fuzzy logic control, genetic algorithms, learning Address correspondence to Luis Magdalena, ETS Ingenieros de Telecomunicaci'on, Universidad Polit'ecn...

This paper describes CES, a proto-type of a new programming language for robots and other embedded systems, equipped with sensors and actuators. CES contains two new ideas, currently not found in other programming languages: support of computing with uncertain information, and support of adaptation and teaching as a means of programming. These innovations facilitate the rapid development of software for embedded systems, as demonstrated by a mobile robot application.

Nowadays, Fuzzy Rule-Based Systems are successfully applied to many different real-world problems. Unfortunatelly, relatively few well-structured methodologies exist for designing them and, in many cases, human experts are not able to express the knowledge needed to solve the problem in the form of fuzzy rules. TSK Fuzzy Rule-Based Systems were enunciated in order to solve this design problem because they are usually identified using numerical data. In this paper we present a two-stage evolutionary process for designing TSK Fuzzy Rule-Based Systems from examples combining a generation stage based on a (¯; )-Evolution Strategy, in which the fuzzy rules with different consequents compete among themselves to form part of a preliminary Knowledge Base, and a refinement stage, in which both the antecedent and consequent parts of the fuzzy rules in this previous Knowledge Base are adapted by a hybrid evolutionary process composed of a Genetic Algorithm and an Evolution Strategy to obtain the ...

Trust management model that we present is adapted for ubiquitous devices cooperation, rather than for classic client-supplier relationship. We use fuzzy numbers to represent trust, to capture both the trust value and its uncertainty. The model contains the trust representation part, decisionmaking part and a learning part. In our representation, we define the trusted agents as a type-2 fuzzy set. In a decisionmaking part, we use the methods from the fuzzy rule computation and fuzzy control domain to take trusting decision. For trust learning, we use a strictly iterative approach, well adapted to constrained environments. We verify our model in a multi-agent simulation where the agents in the community learn to identify defecting members and progressively refuse to cooperate with them. Our simulation contains significant background noise to validate model robustness.