研究了基于自适应特征融合及模块神经网络的手写体汉字识别。
I studied the handwritten Chinese character recognition techniques based on adaptive information fusion and module neural networks.
数据融合方法采用经典的自适应加权融合估计算法，配合智能判别技术，增强了火灾特征识别的可靠性。
Combined with intelligent recognition technology, data fusion technology adopts the classical self-adapting weighting fusion algorithm to increase the reliability of fire characteristics recognition.
并将两种特征模型进行线性融合，得到最终的目标表征模型，其中的融合系数由特征似然图对比度自适应确定。
The final target representation model was obtained by means of linear fusing the two feature models, and the fusion coefficient was determined adaptively by contrast ratio of feature likelihood map.
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