It introduces the system design of mechanical hand using embedded speech recognition. The design mainly includes two parts: hardware design and software design.
介绍了利用嵌入式语音识别技术来控制机械手的系统设计过程，包括两部分硬件设计和软件设计。
The result indicates that based on CP neural network, the fault pattern recognition system has strong nonlinear mapping ability, therefore it can be used to correctly classify the mechanical faults.
结果表明，以CP神经网络构筑的故障模式识别器有很强的非线性映射能力，可对机械设备故障模式进行正确分类。
During the system design, this paper USES the way of orthogonal Fourier-Mellin moments to extract the image feature, and this conducts the reliable recognition of mechanical workpiece.
在不变性模式识别系统设计中，本文采用正交傅立叶-梅林矩提取图像特征的方法，实现了零件形状的可靠识别。
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