A new approach to eye-in-hand image-based visual servoing based on fuzzy modeling and control is proposed in this paper. Fuzzy modeling is applied to obtain an inverse model of the mapping between image features velocities and joints velocities, avoiding the necessity of inverting the Jacobian. An inverse model is identified for each trajectory using measurements data of a robotic manipulator, ...

This paper is about mathematical foundational aspects of fuzzy theory for engineers who wish to take a serious look at fuzzy theory for significant applications. As a tool for modeling and reasoning, fuzzy theory has its own domain of applicability. We provide firm foundations for implementing fuzzy concepts and fuzzy rules, specify the domains where fuzzy theory is appropriate, show firm conne...

An approach to data-driven linguistic modeling is presented. The methodology is based on a fuzzy system with relational input partition that allows for transparent modeling of linear dependencies between the inputs. An identification algorithm for this type of fuzzy system is proposed. It automatically finds strongest dependencies from numerical data. An application example illustrates the usef...

Fuzzy modeling of high-dimensional systems is a challenging topic. This study proposes an effective approach to data-based fuzzy modeling of high-dimensional systems. The proposed method works on the fuzzification layer and tries to use two-dimensional membership functions instead of onedimensional ones. This approach reduces fuzzy rule base radically due to using of two-dimensional membership ...

Constructing concise fuzzy rule bases from databases containing many features present an important yet challenging goal in the current researches of fuzzy rule-based systems. Utilization of all available attributes is not realistic due to the “curse of dimensionality” with respect to the rule number as well as the overwhelming computational costs. This paper proposes a general framework to trea...

This paper proposes fuzzy modeling using obtained data. Fuzzy system is known as knowledge-based or rule-bases system. The most important part of fuzzy system is rule-base. One of problems of generation of fuzzy rule with training data is inconsistence data. Existence of inconsistence and uncertain states in training data causes high error in modeling. Here, Probability fuzzy system presents to...

In this study, a black box modeling of the coupledtank system is obtained by using fuzzy sets. The derived model is tested via adaptive neuro fuzzy inference system (ANFIS). In order to achieve a better control performance, the parameters of three different controller types, classical proportional integral controller (PID), fuzzy PID and function tuner method, are tuned by one of the evolutiona...

Fuzzy logic is a powerful method because of its ability to allow the knowledge in the model to be extracted and interpreted by knowledge system. Fuzzy modeling is an approach which implements the fuzzy logic in the process of modeling the system. But in the construction of the complex system, there is a limitation of fuzzy model due to the large number of related input and output, dealing with ...

In this paper, we investigate on-line fuzzy modeling for predicting the prices of residential premises using the concept of evolving fuzzy models. These combine the aspects of incrementally updating the parameters and expanding the inner structure on demand with the concepts of uncertainty modeling in a possibilistic and linguistic manner (achieved through fuzzy sets and fuzzy rule bases). We u...

This paper presents the use of fuzzy inference to provide a viable modeling and simulation methodology for the estimation of population growth in any country or region. The study is motivated by the classical complex and time-consuming growth modeling and prediction methods. The related design issues are presented and the fuzzy inference model for population growth is derived. The human social ...