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通用医疗

 

 

 

 

 

 

 

 

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Hopfield-Netz
霍普菲爾德神經網絡 霍普菲尔德神经网络
Als Hopfield-Netz bezeichnet man eine besondere Form eines künstlichen neuronalen Netzes, das sich von anderen künstlichen neuronalen Netzen dadurch unterscheidet, dass es ein Rekurrentes neuronales Netz ist, das symmetrisch gewichtete Verbindungen besitzt und als Assoziativspeicher dient, der stabile Zustände (Attraktoren) für Muster erkennt. Es erkennt gespeicherte Muster anhand von Ähnlichkeiten, indem es sich in einen stabilen Zustand bewegt, der einem bekannten Muster entspricht.

霍普菲尔德神经网络(Hopfield neural network)是一种循环神经网络，由约翰·霍普菲尔

Als Hopfield-Netz bezeichnet man eine besondere Form eines künstlichen neuronalen Netzes, das sich von anderen künstlichen neuronalen Netzen dadurch unterscheidet, dass es ein Rekurrentes neuronales Netz ist, das symmetrisch gewichtete Verbindungen besitzt und als Assoziativspeicher dient, der stabile Zustände (Attraktoren) für Muster erkennt. Es erkennt gespeicherte Muster anhand von Ähnlichkeiten, indem es sich in einen stabilen Zustand bewegt, der einem bekannten Muster entspricht.

Es ist nach dem amerikanischen Wissenschaftler John Hopfield benannt, der das Modell 1982 bekannt machte.

Hopfield-Netze hatten ursprünglich eine bedeutende Rolle in der Entwicklung der neuronalen Netzwerke, da sie als frühes Modell für assoziative Speicher und Mustererkennung dienten und wichtige Erkenntnisse zur Energie-Minimierung in neuronalen Netzen lieferten. Heute sind sie weiterhin von Bedeutung als Grundlage für moderne Ansätze in der künstlichen Intelligenz und maschinellem Lernen, insbesondere in der Forschung über rekurrente Netze und Energie-basierte Modelle.

德在1982年发明。Hopfield网络是一种结合存储系统和二元系统的神经网络。它保证了向局部极小的收敛，但收敛到错误的局部极小值（local minimum），而非全局极小（global minimum）的情况也可能发生。霍普菲尔德网络也提供了模拟人类记忆的模型。

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