<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">George Georgiev</style></author><author><style face="normal" font="default" size="100%">Valentine Penev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mathematical Model of Fuzzy Control System for Autonomous Guided Vehicle in 3D Space</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Autonomous guided vehicles.</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic Programming</style></keyword><keyword><style  face="normal" font="default" size="100%">fuzzy control</style></keyword><keyword><style  face="normal" font="default" size="100%">Maximizing decision</style></keyword><keyword><style  face="normal" font="default" size="100%">Policy function</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">195-207</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In previous papers, the authors have described a theoretical approach to the development of mathematical meta-models, which aim to capture the emergent behaviour of intelligent agent-based constructive simulation models of military conflict. These intelligent agents capture the process of C4ISR (Command, Control, Communications, Computers, Intelligence Surveillance and Reconnaissance) in such agent-based simulation models. In this paper, the authors present both historical evidence and evidence from experiments using cellular automata models that support hypotheses derived from their theory.</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Plamena Andreeva</style></author><author><style face="normal" font="default" size="100%">George Georgiev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Fuzzy Control Based on Cluster Analysis and Dynamic Programming</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal </style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Clustering algorithm</style></keyword><keyword><style  face="normal" font="default" size="100%">Decision-making</style></keyword><keyword><style  face="normal" font="default" size="100%">Dynamic programming.</style></keyword><keyword><style  face="normal" font="default" size="100%">fuzzy control</style></keyword><keyword><style  face="normal" font="default" size="100%">Knowledge Based Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning in Fuzzy Environment</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1999</style></year><pub-dates><date><style  face="normal" font="default" size="100%">1999</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">91-107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper focuses on fuzzy control of a class of nonlinear systems, which are characterized by model uncertainty and inequality model constraints. The associated Intelligent Information System (IIS) is designed to store the results from possible training made by an expert and distributed via network. The paper considers cluster analysis for such a system, based on Bezdek’s fuzzy cluster method (FCM). The proposed method is used to classify the input data and to extract the rules. 
An example of fuzzy control for autonomous mobile system in 3D space is explored and the results from the decision using the method of dynamic programming in fuzzy environment are shown. The synthesized algorithm guides an autonomous vehicle in 3D space which pursues an object and evades an obstacle. The fuzzy control is based on determination of a maximizing decision by using dynamic programming. The maximizing decision is defined as a point in the space of alternatives at which the membership function of a fuzzy decision attains its maximum value. The purpose of the presented algorithm is to demonstrate a fuzzy method for determination of the trajectory of the dynamic object.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">George Georgiev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Algorithm for Fuzzy Control of Autonomous Mobile Robot</style></title><secondary-title><style face="normal" font="default" size="100%">INCON'97</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><pub-location><style face="normal" font="default" size="100%">Sofia, Bulgaria</style></pub-location><pages><style face="normal" font="default" size="100%">53-56</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>