Hierarchical fuzzy logic based approach for object tracking

In this paper a novel tracking approach based on fuzzy concepts is introduced. A methodology for both single and multiple object tracking is presented. The aim of this methodology is to use these concepts as a tool to, while maintaining the needed accuracy, reduce the complexity usually involved in object tracking problems. Several dynamic fuzzy sets are constructed according to both kinematic and non-kinematic properties that distinguish the object to be tracked. Meanwhile kinematic related fuzzy sets model the object's motion pattern, the non-kinematic fuzzy sets model the object's appearance. The tracking task is performed through the fusion of these fuzzy models by means of an inference engine. This way, object detection and matching steps are performed exclusively using inference rules on fuzzy sets. In the multiple object methodology, each object is associated with a confidence degree and a hierarchical implementation is performed based on that confidence degree.info:eu-repo/semantics/publishedVersio

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,

Application of Discrete Sets in the Risk Theory

The paper presents an application of the fuzzy sets theory and of the subtle sets in order to evaluate the bankruptcy risk of an organization. The main influence factors of the two antithetical concepts: the gain and the risk of an organization are set. Then, the membership degree of firm activity to gain, respectively to risk is evaluated and the comparison is made. Thus, it results either a favorable condition or a risk of bankruptcy. A numerical application is presented, with a view to understand the described method.systematic risk; fuzzy theory; dynamic index; average index; discrete sets theory

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System

Approximate Reasoning in the Knowledge-based Dynamic Fuzzy Sets

Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system. Ke

Approximate Reasoning in the Knowledge-Based Dynamic Fuzzy Sets

Intan and Mukaidono discussed that knowledge plays an important role in determining the membership function of a given fuzzy set by introducing a concept, called Knowledge-based Fuzzy Sets (KFS) in 2002. Here, the membership degree of an element given a fuzzy set is subjectively determined by the knowledge. Every knowledge may have each different membership degree of the element given the fuzzy set. In 1988, Wang et al. extended the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS) by considering that the membership degree of an element given a fuzzy set might be dynamically changeable over the time. Both generalized concepts, KFS and DFS, were hybridized by Intan et al. to be a Knowledge-based Dynamic Fuzzy Set (KDFS). As usually happened in the real-world application, the KDFS showed that a membership function of a given fuzzy set subjectively determined by a certain knowledge may be dynamically changeable over time. Moreover, the concept of fuzzy granularity was discussed dealing with the KDFS. Related to the concept of fuzzy granularity in KDFS, this paper discusses the concept of approximate reasoning of KDFS in representing fuzzy production rules as generally applied in the fuzzy expert system

The Fuzzy and Dynamic Nature of Trust

Trust is one of the most fuzzy, dynamic and complex concepts in both social and business relationships. The difficulty in measuring Trust and predicting Trustworthiness in service-oriented network environments leads to many questions. These include issues such as how to measure the willingness and capability of individuals in the Trust dynamic and how to assign a concrete level of Trust to an individual or Agent. In this paper, we analyze the fuzzy, dynamic and complex nature of Trust.The dynamic nature of Trust creates the biggest challenge in measuring Trust and predicting Trustworthiness. In order to develop a Trustworthiness Measure and Prediction Method, we first need to understand what we can actually measure in a Trust Relationship

An Intelligent Routing Protocol Based on DYMO for MANET

in this paper, intelligent routing protocols for mobile ad-hoc networks (MANET) will be proposed .Depending on the concepts of fuzzy and neural networks. The goal is to get good quality service by finding the most convenient data transfer paths, therefore a Fuzzy-based, Neural-Fuzzy based and Energy aware are three approaches have been proposed to enhance Dynamic Manet On-demand (DYMO),All approaches were implemented in ns-2 simulator and compared with original protocol in terms of performance metrics, which showed that there was an improvement in route efficiency

Fuzzy Granularity in the Knowledge-based Dynamic Fuzzy Sets

In 2002, Intan and Mukaidono proposed Knowledge-based Fuzzy Sets (KFS) as an extended concept of the fuzzy set. Here, the membership function of a fuzzy set is subjectively determined by the knowledge. Wang et al. (1988) generalized the concept of fuzzy set, called Dynamic Fuzzy Sets (DFS). In the DFS, the membership degree of an element might dynamically change according to the times variable. Both extended concepts of fuzzy sets were then combined by Intan et al. to be a hybrid concept, called Knowledge-based Dynamic Fuzzy Set. The concept is regarded as a more generalization of fuzzy sets by considering that the membership function of a given fuzzy set provided by a certain knowledge may be dynamically changed over time as usually happened in the real-world application. To continually extend the concept of knowledge-based dynamic fuzzy sets, this paper discusses how the fuzzy granularity is constructed in the knowledge-based dynamic fuzzy sets. The concepts of objectivity and consistency are discussed, along with their proposed measures