1) semi fuzzy vector quantization (SFVQ)
半模糊矢量量化
1.
On the basis of fuzzy C means (FCM) and vector quantization (VQ), the semi fuzzy vector quantization (SFVQ) is given to improve the preprocessing stage of RBFNN.
提出一种基于半模糊矢量量化 (SFVQ)技术的改进径向基函数神经网 (IRBFNN)分类器 ,并且用于无约束手写体数字的识别 。
2) fuzzy kernel vector quantization
模糊核矢量量化
3) fuzzy vector quantization
模糊矢量量化
1.
In this poper, a new speech recognition approach based on fuzzy vector quantization andfuzzy training of HMMs is presented.
本文提出了一种基于模糊矢量量化（ＦＶＱ）和隐马尔柯夫模型（ＨＭＭ）模糊训练的语音识别新方法。
4) Batch fuzzy learn vector quantization
批量模糊学习矢量量化
5) fuzzy kernel entropy vector quantization
模糊核熵矢量量化
1.
By defining the fuzzy kernel entropy objection function in the feature space, fuzzy kernel entropy vector quantization was designed to train speakers’ models and make identification decision in high-dimensional feature space.
通过定义特征空间中的模糊核熵目标函数,设计了模糊核熵矢量量化器,在高维特征空间中对说话人的语音进行训练和识别匹配。
6) fuzzy-vector control
模糊矢量控制
1.
The fuzzy-vector control method and direct speed control were applied.
CAN通信模块采用新型CAN总线收发器TJA1040和独立CAN控制器,并采用各种抗干扰措施以保证CAN通信的稳定性;控制模块利用模糊矢量控制方案——直接速度控制(direct speed control,DSC)策略,实现电驱动系统高鲁棒性控制。
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