【摘要】 本文提出了一种全局最优的神经网络（ＦｕｌｌＤｏｍａｉｎＯｐｔｉｍｕｍＮｅｕｒａｌＮｅｔｗｏｒｋ）模型用于目标识别。通常所设计的神经网络不能保证全局最优，使得网络不一定收敛到期望样本点上。本文的模型采用了先设计稳定点、再构造吸引域的方法，提高了网络的识别正确率及速度。针对图像识别中矢量维数大的实际，提出了一种不变性方法，使得样本维数下降而分类距离保持不变。同时又证明了网络的收敛性、收敛速度及映射保距等。计算机模拟结果表明，网络对噪声或缺损图均能正确识别。更多还原

【Abstract】 A foll domain optimum neural network(FDONN)and its application to image rec-ognition are proposed in this paper.A neural network generally cannot converge to a global mini-mum.In this paper,a method of devising stable points firstly and basins of attraction laterly is used,by which the speed and correctness of recognition are improved.Owing to the fact that the dimen-sions of image are large,an invariant transformation method is presented to decrease the dimen-sions of image without changing the distance between them. The properties of the neural network,such as quality and speed of convergence and invariance of mapping, are proved,too. Several com-puter simulation examples are given. The recognised images include noise-added images and defec-tive images.更多还原