Analyse und Prognose des Modellzyklusses in der Automobilbranche mit künstlichen neuronalen Netzen
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Inhaltsangabe: Gang der Untersuchung: Künstliche neuronale Netze (KNN) eignen sich neben Einsätzen im Finanzmarktbereich auch für die Abbildung von Zusammenhängen im Automobilbereich. Zunächst werden die Grundlagen zu Langfristprognosen und KNN dargestellt sowie die Modellzyklen, die sich im deutschen Automobilmarkt identifizieren lassen, detailliert untersucht. Darauf aufbauend wird ein Modell entwickelt, das es erlaubt, die identifizierten Spezifika von Modellzyklen mittels KNN zu repräsentieren. Besonderes Augenmerk liegt dabei auf der Systematisierung der Gestaltungsalternativen vom Ausgangsproblem bis hin zur Implementierung eines KNN für diese Aufgabe. In umfangreichen empirischen Tests werden unterschiedlich konfigurierte Backpropagation-Netze auf ihre Zweckmä igkeit hin geprüft. Die Ergebnisse werden abschlie end zusammengefa t und Ansätze zu Weiterentwicklungen aufgezeigt. Inhaltsverzeichnis: Inhaltsverzeichnis: InhaltsverzeichnisI AbbildungsverzeichnisV AbkürzungsverzeichnisVIII SymbolverzeichnisX VorwortXII I.Motivation des Einsatzes neuronaler Netze zur Prognose1 II.Aspekte langfristiger Prognosen4 1.Definition und Gegenstand von Absatzprognosen4 2.Methodische Grundlagen für Prognosemodelle5 3.Berücksichtigung der Unsicherheit7 4.Bewertung von Prognosemodellen9 III.Grundlagen Künstlicher neuronaler Netze10 1.Kleine Genealogie neuronaler Netze10 2.Überblick über die ein KNN spezifizierenden Parameter12 2.1Aufbau und Funktionsweise künstlicher Neuronen12 2.2Netztopologie16 2.3Lernen in Künstlichen neuronalen Netzen18 3.Die Verbindung neuronaler Netze mit genetischen Algorithmen23 IV.Modellzyklen im deutschen Automobilmarkt27 1.Das theoretische Konzept27 1.1Vom Produktlebenszyklus zum Modellzyklus27 1.2Zur Abgrenzung von Modellzyklen30 1.3Kritikpunkte31 1.4Die Determinanten von Modellzyklen32 1.5Die Relevanz für die Automobilindustrie32 2.Die Entwicklungen der letzten 30 Jahre34 2.1Die Länge der Modellzyklen im deutschen Aut
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