{"id":{"repo_id":"greece","oai_identifier":"oai:10442/0001"},"canonical_url":"https://search.dev.ndltd.org/etd/greece/oai:10442/0001","repository":{"repo_id":"greece","name":"Greek National Archive of PhD Theses","base_url":"https://phdtheses.ekt.gr/eadd_oai/request"},"display":{"title":"ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ-ΣΥΓΚΡΙΤΙΚΗ ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ","abstract":"THIS THESIS IS DEALING WITH LINEAR ESTIMATION PARTITIONING ALGORITHMS, FOR DISCRETE TIME SYSTEMS. FOUR NEW RECURSIVE PER- SAMPLE PARTITIONING ESTIMATION ALGORITHMS ARE INITIALY PRESENTED, AND THE RELATED MERITS, WITH RESPECT TO THEIR COMPUTATIONAL REQUIREMENTS ARE EXAMINED AGAINST THREE OTHER PARTITIONING ALGORITHMS (LAINIOTIS GENERALIZED PARTITIONING ALGORITHM, RECURSIVE PARTITIONING ALGORITHM OF ANDRISANI, GAU AND THE LAINIOTIS PER-SAMPLE PARTITIONING FILTER) AND THE KALMAN FILTER. THIS COMPARATIVE STUDY IS CONSIDERED FOR BOTH TIME INVARIANT ANDTIME VARYING SYSTEMS. FINALLY THE BEST OF THESE ALGORITHMS WITH RESPECT TO COMPUTER TIME AND STORAGE REQUIREMENTS ARE APPLIED TO THE MULTISENSOR ESTIMATION PROBLEMS.","abstract_html":"THIS THESIS IS DEALING WITH LINEAR ESTIMATION PARTITIONING ALGORITHMS, FOR DISCRETE TIME SYSTEMS. FOUR NEW RECURSIVE PER- SAMPLE PARTITIONING ESTIMATION ALGORITHMS ARE INITIALY PRESENTED, AND THE RELATED MERITS, WITH RESPECT TO THEIR COMPUTATIONAL REQUIREMENTS ARE EXAMINED AGAINST THREE OTHER PARTITIONING ALGORITHMS (LAINIOTIS GENERALIZED PARTITIONING ALGORITHM, RECURSIVE PARTITIONING ALGORITHM OF ANDRISANI, GAU AND THE LAINIOTIS PER-SAMPLE PARTITIONING FILTER) AND THE KALMAN FILTER. THIS COMPARATIVE STUDY IS CONSIDERED FOR BOTH TIME INVARIANT ANDTIME VARYING SYSTEMS. FINALLY THE BEST OF THESE ALGORITHMS WITH RESPECT TO COMPUTER TIME AND STORAGE REQUIREMENTS ARE APPLIED TO THE MULTISENSOR ESTIMATION PROBLEMS.","abstract_has_math":false,"creators":["Αναγνώστου, Κωνσταντίνος"],"institution":"University of Patras","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1985,"date_issued":"1985","date_published":"1985","updated_at":"2026-07-24T02:24:43Z","subjects":["ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ","Αλγόριθμοι εκτίμησης","ΓΡΑΜΜΙΚΗ ΕΚΤΙΜΗΣΗ","Δυναμικά συστήματα","Θεωρία εκτίμησης","Υπολογιστική ανάλυση","ΦΙΛΤΡΑ LAINIOTIS","Φιλτράρισμα","Φίλτρο Kalman","Εκτίμηση","Computational analysis","Dynamic systems","Estimation algorithms","Estimation theory","Filtering","Kalman filter","LAINIOTIS FILTERS","LINEAR ESTIMATION","Φυσικές Επιστήμες","Επιστήμη Ηλεκτρονικών Υπολογιστών και Πληροφορική","Επιστήμες Μηχανικού και Τεχνολογία","Επιστήμη Ηλεκτρολόγου Μηχανικού, Ηλεκτρονικού Μηχανικού, Μηχανικού Η/Υ","Natural Sciences","Computer and Information Sciences","Engineering and Technology","Electrical Engineering, Electronic Engineering, Information Engineering"],"languages":["gre"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.12681/eadd/0001"],"render_values":[{"text":"10.12681/eadd/0001","href":"https://doi.org/10.12681/eadd/0001","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10442/hedi/0001","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Αναγνώστου, Κωνσταντίνος"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["1985"]},{"key":"dc:publisher","label":"Institution","values":["University of Patras","Πανεπιστήμιο Πατρών"]},{"key":"dc:type","label":"Dc Type","values":["PhD Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ","Αλγόριθμοι εκτίμησης","ΓΡΑΜΜΙΚΗ ΕΚΤΙΜΗΣΗ","Δυναμικά συστήματα","Θεωρία εκτίμησης","Υπολογιστική ανάλυση","ΦΙΛΤΡΑ LAINIOTIS","Φιλτράρισμα","Φίλτρο Kalman","Εκτίμηση","Computational analysis","Dynamic systems","Estimation algorithms","Estimation theory","Filtering","Kalman filter","LAINIOTIS FILTERS","LINEAR ESTIMATION","Φυσικές Επιστήμες","Επιστήμη Ηλεκτρονικών Υπολογιστών και Πληροφορική","Επιστήμες Μηχανικού και Τεχνολογία","Επιστήμη Ηλεκτρολόγου Μηχανικού, Ηλεκτρονικού Μηχανικού, Μηχανικού Η/Υ","Natural Sciences","Computer and Information Sciences","Engineering and Technology","Electrical Engineering, Electronic Engineering, Information Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["gre"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10.12681/eadd/0001","http://hdl.handle.net/10442/hedi/0001"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["THIS THESIS IS DEALING WITH LINEAR ESTIMATION PARTITIONING ALGORITHMS, FOR DISCRETE TIME SYSTEMS. FOUR NEW RECURSIVE PER- SAMPLE PARTITIONING ESTIMATION ALGORITHMS ARE INITIALY PRESENTED, AND THE RELATED MERITS, WITH RESPECT TO THEIR COMPUTATIONAL REQUIREMENTS ARE EXAMINED AGAINST THREE OTHER PARTITIONING ALGORITHMS (LAINIOTIS GENERALIZED PARTITIONING ALGORITHM, RECURSIVE PARTITIONING ALGORITHM OF ANDRISANI, GAU AND THE LAINIOTIS PER-SAMPLE PARTITIONING FILTER) AND THE KALMAN FILTER. THIS COMPARATIVE STUDY IS CONSIDERED FOR BOTH TIME INVARIANT ANDTIME VARYING SYSTEMS. FINALLY THE BEST OF THESE ALGORITHMS WITH RESPECT TO COMPUTER TIME AND STORAGE REQUIREMENTS ARE APPLIED TO THE MULTISENSOR ESTIMATION PROBLEMS.","Η ΔΙΑΤΡΙΒΗ ΑΥΤΗ ΔΙΑΠΡΑΓΜΑΤΕΥΕΤΑΙ ΑΛΓΟΡΙΘΜΟΥΣ ΔΙΑΜΕΛΙΣΜΟΥ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ ΓΙΑ ΣΥΣΤΗΜΑΤΑ ΔΙΑΚΡΙΤΟΥ ΧΡΟΝΟΥ. ΚΑΤ' ΑΡΧΗΝ ΠΑΡΟΥΣΙΑΖΟΝΤΑΙ ΤΕΣΣΕΡΙΣ ΝΕΟΙ ΑΝΑΔΡΟΜΙΚΟΙ ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ ΑΝΑ ΔΕΙΓΜΑ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ. ΣΤΗ ΣΥΝΕΧΕΙΑ ΓΙΝΕΤΑΙ ΣΥΓΚΡΙΤΙΚΗ ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ ΤΩΝ ΝΕΩΝ ΑΥΤΩΝ ΑΛΓΟΡΙΘΜΩΝ, ΜΕ ΤΡΕΙΣ ΑΛΛΟΥΣ ΑΛΓΟΡΙΘΜΟΥΣ ΔΙΑΜΕΛΙΣΜΟΥ (ΤΟ ΓΕΝΙΚΕΥΜΕΝΟ ΑΛΓΟΡΙΘΜΟ ΔΙΑΜΕΛΙΣΜΟΥ ΤΟΥ ΛΑΙΝΙΩΤΗ, ΤΟΝ ΑΝΑΔΡΟΜΙΚΟ ΑΛΓΟΡΙΘΜΟ ΔΙΑΜΕΛΙΣΜΟΥ ΤΩΝ ANDRISANI, GUA ΚΑΙ ΤΟ ΓΡΑΜΜΙΚΟ ΦΙΛΤΡΟ ΔΙΑΜΕΛΙΣΜΟΥ ΑΝΑ ΔΕΙΓΜΑ ΤΟΥ ΛΑΙΝΙΩΤΗ) ΚΑΙ ΜΕ ΤΟΝ ΑΛΓΟΡΙΘΜΟ (ΦΙΛΤΡΟ) KALMAN, ΣΧΕΤΙΚΑ ΜΕ ΤΙΣ ΥΠΟΛΟΓΙΣΤΙΚΕΣ ΤΩΝ ΑΠΑΙΤΗΣΕΙΣ ΑΠΟΘΗΚΕΥΣΗΣ ΚΑΙ ΧΡΟΝΟΥ, ΤΟΣΟ ΓΙΑ ΧΡΟΝΙΚΑ ΑΜΕΤΑΒΛΗΤΑ ΟΣΟ ΚΑΙ ΓΙΑ ΧΡΟΝΙΚΑ ΜΕΤΑΒΑΛΛΟΜΕΝΑ ΜΟΝΤΕΛΑ. ΤΕΛΟΣ ΟΙ ΟΙΚΟΝΟΜΙΚΟΤΕΡΟΙ (ΑΠΟ ΑΠΟΨΗ ΑΠΑΙΤΗΣΕΩΝ ΑΠΟΘΗΚΕΥΣΗΣ) ΚΑΙ ΤΑΧΥΤΕΡΟΙ ΑΠΟ ΤΟΥΣ ΑΛΓΟΡΙΘΜΟΥΣ ΑΥΤΟΥΣ, ΕΦΑΡΜΟΖΟΝΤΑΙ ΣΤΟ ΠΡΑΚΤΙΚΟ ΠΡΟΒΛΗΜΑ ΤΗΣ ΕΚΤΙΜΗΣΗΣ ΤΩΝ ΠΟΛΛΩΝ ΑΙΣΘΗΤΗΡΩΝ."]},{"key":"dc:title","label":"Title","values":["ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ-ΣΥΓΚΡΙΤΙΚΗ ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ","LINEAR PARTINIONING ESTIMATION ALGORITHMS-COMPARATIVE COMPUTATIONAL ANALYSIS"]}]}],"canonical_facts":{"dc:creator":["Αναγνώστου, Κωνσταντίνος"],"dc:date":["1985"],"dc:description":["THIS THESIS IS DEALING WITH LINEAR ESTIMATION PARTITIONING ALGORITHMS, FOR DISCRETE TIME SYSTEMS. FOUR NEW RECURSIVE PER- SAMPLE PARTITIONING ESTIMATION ALGORITHMS ARE INITIALY PRESENTED, AND THE RELATED MERITS, WITH RESPECT TO THEIR COMPUTATIONAL REQUIREMENTS ARE EXAMINED AGAINST THREE OTHER PARTITIONING ALGORITHMS (LAINIOTIS GENERALIZED PARTITIONING ALGORITHM, RECURSIVE PARTITIONING ALGORITHM OF ANDRISANI, GAU AND THE LAINIOTIS PER-SAMPLE PARTITIONING FILTER) AND THE KALMAN FILTER. THIS COMPARATIVE STUDY IS CONSIDERED FOR BOTH TIME INVARIANT ANDTIME VARYING SYSTEMS. FINALLY THE BEST OF THESE ALGORITHMS WITH RESPECT TO COMPUTER TIME AND STORAGE REQUIREMENTS ARE APPLIED TO THE MULTISENSOR ESTIMATION PROBLEMS.","Η ΔΙΑΤΡΙΒΗ ΑΥΤΗ ΔΙΑΠΡΑΓΜΑΤΕΥΕΤΑΙ ΑΛΓΟΡΙΘΜΟΥΣ ΔΙΑΜΕΛΙΣΜΟΥ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ ΓΙΑ ΣΥΣΤΗΜΑΤΑ ΔΙΑΚΡΙΤΟΥ ΧΡΟΝΟΥ. ΚΑΤ' ΑΡΧΗΝ ΠΑΡΟΥΣΙΑΖΟΝΤΑΙ ΤΕΣΣΕΡΙΣ ΝΕΟΙ ΑΝΑΔΡΟΜΙΚΟΙ ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ ΑΝΑ ΔΕΙΓΜΑ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ. ΣΤΗ ΣΥΝΕΧΕΙΑ ΓΙΝΕΤΑΙ ΣΥΓΚΡΙΤΙΚΗ ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ ΤΩΝ ΝΕΩΝ ΑΥΤΩΝ ΑΛΓΟΡΙΘΜΩΝ, ΜΕ ΤΡΕΙΣ ΑΛΛΟΥΣ ΑΛΓΟΡΙΘΜΟΥΣ ΔΙΑΜΕΛΙΣΜΟΥ (ΤΟ ΓΕΝΙΚΕΥΜΕΝΟ ΑΛΓΟΡΙΘΜΟ ΔΙΑΜΕΛΙΣΜΟΥ ΤΟΥ ΛΑΙΝΙΩΤΗ, ΤΟΝ ΑΝΑΔΡΟΜΙΚΟ ΑΛΓΟΡΙΘΜΟ ΔΙΑΜΕΛΙΣΜΟΥ ΤΩΝ ANDRISANI, GUA ΚΑΙ ΤΟ ΓΡΑΜΜΙΚΟ ΦΙΛΤΡΟ ΔΙΑΜΕΛΙΣΜΟΥ ΑΝΑ ΔΕΙΓΜΑ ΤΟΥ ΛΑΙΝΙΩΤΗ) ΚΑΙ ΜΕ ΤΟΝ ΑΛΓΟΡΙΘΜΟ (ΦΙΛΤΡΟ) KALMAN, ΣΧΕΤΙΚΑ ΜΕ ΤΙΣ ΥΠΟΛΟΓΙΣΤΙΚΕΣ ΤΩΝ ΑΠΑΙΤΗΣΕΙΣ ΑΠΟΘΗΚΕΥΣΗΣ ΚΑΙ ΧΡΟΝΟΥ, ΤΟΣΟ ΓΙΑ ΧΡΟΝΙΚΑ ΑΜΕΤΑΒΛΗΤΑ ΟΣΟ ΚΑΙ ΓΙΑ ΧΡΟΝΙΚΑ ΜΕΤΑΒΑΛΛΟΜΕΝΑ ΜΟΝΤΕΛΑ. ΤΕΛΟΣ ΟΙ ΟΙΚΟΝΟΜΙΚΟΤΕΡΟΙ (ΑΠΟ ΑΠΟΨΗ ΑΠΑΙΤΗΣΕΩΝ ΑΠΟΘΗΚΕΥΣΗΣ) ΚΑΙ ΤΑΧΥΤΕΡΟΙ ΑΠΟ ΤΟΥΣ ΑΛΓΟΡΙΘΜΟΥΣ ΑΥΤΟΥΣ, ΕΦΑΡΜΟΖΟΝΤΑΙ ΣΤΟ ΠΡΑΚΤΙΚΟ ΠΡΟΒΛΗΜΑ ΤΗΣ ΕΚΤΙΜΗΣΗΣ ΤΩΝ ΠΟΛΛΩΝ ΑΙΣΘΗΤΗΡΩΝ."],"dc:identifier":["10.12681/eadd/0001","http://hdl.handle.net/10442/hedi/0001"],"dc:language":["gre"],"dc:publisher":["University of Patras","Πανεπιστήμιο Πατρών"],"dc:subject":["ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ","Αλγόριθμοι εκτίμησης","ΓΡΑΜΜΙΚΗ ΕΚΤΙΜΗΣΗ","Δυναμικά συστήματα","Θεωρία εκτίμησης","Υπολογιστική ανάλυση","ΦΙΛΤΡΑ LAINIOTIS","Φιλτράρισμα","Φίλτρο Kalman","Εκτίμηση","Computational analysis","Dynamic systems","Estimation algorithms","Estimation theory","Filtering","Kalman filter","LAINIOTIS FILTERS","LINEAR ESTIMATION","Φυσικές Επιστήμες","Επιστήμη Ηλεκτρονικών Υπολογιστών και Πληροφορική","Επιστήμες Μηχανικού και Τεχνολογία","Επιστήμη Ηλεκτρολόγου Μηχανικού, Ηλεκτρονικού Μηχανικού, Μηχανικού Η/Υ","Natural Sciences","Computer and Information Sciences","Engineering and Technology","Electrical Engineering, Electronic Engineering, Information Engineering"],"dc:title":["ΑΛΓΟΡΙΘΜΟΙ ΔΙΑΜΕΛΙΣΜΟΥ ΓΡΑΜΜΙΚΗΣ ΕΚΤΙΜΗΣΗΣ-ΣΥΓΚΡΙΤΙΚΗ ΥΠΟΛΟΓΙΣΤΙΚΗ ΑΝΑΛΥΣΗ","LINEAR PARTINIONING ESTIMATION ALGORITHMS-COMPARATIVE COMPUTATIONAL ANALYSIS"],"dc:type":["PhD Thesis"]},"updated_at":"2026-07-24T02:24:43Z"}