Download Computational Intelligence: Eine methodische Einfuhrung in by Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian PDF

By Rudolf Kruse, Christian Borgelt, Frank Klawonn, Christian Moewes, Georg Ruß, Matthias Steinbrecher

Der Anwendungsbereich „Computational Intelligence“ der Informatik erlangt durch viele erfolgreiche industrielle Produkte immer mehr an Bedeutung. Dieses Buch behandelt die zentralen Techniken dieses Gebiets und bettet sie in ein didaktisches Konzept ein, welches sich gezielt an Studierende und Lehrende der Informatik wendet. Zusatzmaterialien wie Aufgaben, Lösungen und Foliensätze für Vorlesungen sowie Beispiele aus der industriellen Anwendung betonen den praktischen Charakter des Buches.

Der Inhalt
Künstliche Neuronale Netze – Fuzzy-Systeme – Evolutionäre Algorithmen – Bayes-Netze

Die Zielgruppe
- Studierende der Informatik, Bachelor und Master
- Lehrende der Informatik
- Forscher in Bereich Computational Intelligence

Die Autoren
Das Autorenteam (Prof. Dr. Rudolf Kruse, PD Dr. Christian Borgelt, Prof. Dr. Frank Klawonn, Christian Moewes, Georg Ruß und Matthias Steinbrecher) bringt langjährige Erfahrung und fachliche attractiveness im Gebiet Computational Intelligence mit – aus Universität und Industrie gleichermaßen.

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Read or Download Computational Intelligence: Eine methodische Einfuhrung in Kunstliche Neuronale Netze, Evolutionare Algorithmen, Fuzzy-Systeme und Bayes-Netze PDF

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Extra resources for Computational Intelligence: Eine methodische Einfuhrung in Kunstliche Neuronale Netze, Evolutionare Algorithmen, Fuzzy-Systeme und Bayes-Netze

Example text

X n ) ein Eingabevektor eines Schwellenwertelementes, o die für diesen Eingabevektor gewünschte Ausgabe (output) und y die tatsächliche Ausgabe des Schwellenwertelementes. Ist y = o, dann wird zur Verringerung des Fehlers der Schwellenwert θ und der Gewichtsvektor w = (w1 , . . , wn ) wie folgt verändert: ∀i ∈ {1, . . , n} : θ (neu) = θ (alt) + ∆θ (neu) (alt) wi = wi + ∆wi mit mit ∆θ = −η (o − y), ∆wi = η (o − y) x i , wobei η ein Parameter ist, der Lernrate genannt wird. Er bestimmt die Stärke der Gewichtsänderungen.

M. Weiter mögen beliebige Gewichte w und ein beliebiger Schwellenwert θ gegeben sein (z. B. zufällig bestimmt). 5. Training der Parameter if (w x ≥ θ ) then y := 1; else y := 0; if (y = o ) then begin θ : = θ − η ( o − y ); w := w + η (o − y) x; e : = e + | o − y |; end; end; until (e ≤ 0); end; 27 (* berechne die Ausgabe *) (* des Schwellenwertelementes *) (* wenn die Ausgabe falsch ist *) (* passe den Schwellenwert *) (* und die Gewichte an *) (* summiere die Fehler *) (* wiederhole die Berechnungen *) (* bis der Fehler verschwindet *) Dieser Algorithmus wendet offenbar immer wieder die Delta-Regel an, bis die Summe der Fehler über alle Trainingsbeispiele verschwindet.

5 anhand des Online-Lernvorganges für die Biimplikation gezeigt. Epoche 2 und 3 sind identisch und werden sich folglich endlos wiederholen, ohne dass eine Lösung gefunden wird. Dies ist aber auch nicht verwunderlich, da der Lernvorgang ja erst abgebrochen wird, wenn die Summe der Fehler über alle Trainingsbeispiele verschwindet. 3, dass es kein Schwellenwertelement gibt, das die Biimplikation berechnet. Folglich kann der Fehler gar nicht verschwinden, der Algorithmus nicht terminieren. 30 Kapitel 3.

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