According to the poor adaptive ability of traditional filter algorithm in the estimation for traffic state and travel time with Kalman filter, an improved fuzzy adaptive Kalman filtering method was proposed. The new interest of observation noise was defined, and the fuzzy logic was used to adjust the importance weights of system noise and observation noise through on-line monitoring the interes...

This article discusses the development of a sensor fusion system for guiding an autonomous vehicle through citrus grove alleyways. The sensor system for path finding consists of machine vision and laser radar. An inertial measurement unit (IMU) is used for detecting the tilt of the vehicle, and a speed sensor is used to find the travel speed. A fuzzy logic enhanced Kalman filter was developed t...

Purpose This paper aims to propose a multi-agent approach adaptive control of fixed-wing unmanned aerial vehicles (UAVs) tracking moving ground target. The implies that the UAVs in single group must maintain preset phase shift angles while rotating around target so as evaluate target’s movement more accurately. Thus, controls should ensure UAV swarm follows circular path whose center is also at...

Content-based image retrieval is one of the main research directions in information retrieval and digital library. However, the low-level image features used in traditional content-based image systems lack of semantics meanings of users. In this paper, we propose a multi-layer image data model to combine the low-level image features and high-level linguistic meanings. We analyze and represent a...

This paper presents a new hybrid neuro-fuzzy model which is capable of learning structure and parameters by means of recursive binary space partitioning BSP. Introduction Neuro-fuzzy systems (NFSs) [1] combine the learning ability of artificial neural nets (ANNs) with the linguistic interpretation capacity of fuzzy inference systems (FISs) [2]. This work makes use of BSP (Binary Space Partition...

There are fast known algorithms to compute the transitive closure of a fuzzy relation, but there are only a few different algorithms that compute T-transitive low approximations of a fuzzy relation. A fast method to compute a T-transitive low approximation of a reflexive and symmetric fuzzy relation is given for any continuous tnorm, spending O(n) time.

Exploiting the information in low quality datasets has been recently acknowledged as a new challenge in Genetic Fuzzy Systems. Owing to this, in this paper we discuss the basic principles that govern the extension of a fuzzy rule based classifier to interval and fuzzy data. We have also applied these principles to the genetic learning of a simple cooperative-competitive algorithm, that becomes ...