Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

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低阈值宽调谐PPLN光参量振荡

用半导体激光 (LD)抽运的声光调QNd∶YVO4 激光器做抽运源 ,实现了准位相匹配的光参量输出 ,其调谐范围为 1 4 36～ 1 7μm。非线性光学介质是多周期极化的LiNbO3 (PPLN)。光参量振荡阈值 10 3μJ(脉宽 2 2ns) ,在抽运光达到阈值 3 3倍的条件下 ,信号光输出能量 4 2 5 μJ ,斜效率 12 5 %。     

用AT89C2051设计的PC键盘

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DXDK800自动充填包装机料袋长度的调整

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