三维异联想Hopfield模型

三维物体的识别是有重要应用意义的课题，神经网络模型是解决该复杂问题的一个途径。作者将神经网络模型表达为矩阵形式，提出异联想矩阵形式的Ｈｏｆｉｅｌｄ模型，从而提出了能较好地解决这一问题的新方案。与传统Ｈｏｐｆｉｅｌｄ模型相比较，该模型对三维物体的识别有较好的识别率，对神经网络的发展和实际应用有重要参考价值。

Three Dimensional Heteroassociative Hopfield Model

Recognition of three dimensional object is always an interesting subject. Neural network model with its associative capability is an important approach to solving such complex problem. This paper puts forward a heteroassociative Hopfield model in matrix form, which can deal with recognition of three dimensional object with a much smaller interconnection matrix than the original Hopfield model. Thus, this result should be interesting for the development of neural newtork models and for practical applications.