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A Hopfield network is a form of recurrent artificial neural network popularized by John Hopfield in 1982, but described earlier by Little in 1974. Hopfield nets serve as content-addressable memory systems with binary threshold nodes. They are guaranteed to converge to a local minimum, but convergence to a false pattern (wrong local minimum) rather than the stored pattern (expected local minimum) can occur. Hopfield networks also provide a model for understanding human memory. ==Structure== The units in Hopfield nets are binary threshold units, i.e. the units only take on two different values for their states and the value is determined by whether or not the units' input exceeds their threshold. Hopfield nets normally have units that take on values of 1 or -1, and this convention will be used throughout this page. However, other literature might use units that take values of 0 and 1. Every pair of units ''i'' and ''j'' in a Hopfield network have a connection that is described by the connectivity weight . In this sense, the Hopfield network can be formally described as a complete undirected graph , where is a set of McCulloch-Pitts neurons and is a function that links pairs of nodes to a real value, the connectivity weight. The connections in a Hopfield net typically have the following restrictions: * (no unit has a connection with itself) * (connections are symmetric) The requirement that weights be symmetric is typically used, as it will guarantee that the energy function decreases monotonically while following the activation rules, and the network may exhibit some periodic or chaotic behaviour if non-symmetric weights are used. However, Hopfield found that this chaotic behavior is confined to relatively small parts of the phase space, and does not impair the network's ability to act as a content-addressable associative memory system. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Hopfield network」の詳細全文を読む スポンサード リンク
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