SOFM

基本解释

An image fusion binarization method based on Selforganization Feature Map(SOFM) neural network is presented.
提出了一种基于自组织特征映射(SOFM)神经网络的图像融合二值化方法。
An autonomous star pattern recognition method using the tri-star clustering function of SOFM (Self-Organizing Feature Maps) network is described.
介绍了一种利用SOFM(自组织特征映射)网络的聚类功能进行全天星图识别的算法。
Experiment results show that，compared with LBG algorithm，modified SOFM algorithm can greatly improve the synthesized speech quality in the aspect of subjective and objective.
实验表明，与传统LBG算法比较，采用SOFM神经网络训练的码书其合成语音的主、客观质量均有较大提高。
The results indicate that the accuracy of identification is high and the SOFM network can be used in lithological identification of logging data, which has good prospects.
结果表明，识别的准确率较高，证明SOFM网络可以用于解决测井岩性识别问题，具有很好的应用前景。
Then an example is used to show how to build up an SOFM network model for logging lithological identification and its application to logging lithological identification.
在说明SOFM网络的模型和算法的基础上，结合某地的实际测井资料，建立SOFM网络岩性识别模型，进行岩性识别的应用研究。