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神经网络在压合过程中的应用
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2009-06-23
YYXIAO
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针对将圆锥体压入薄壁筒的控制技术中， 对位移传感器和压力传感器在线实时检测及执行机构气液增力缸的控制问题， 在分析受外界干扰的情况下， 为保证系统的稳定性和收敛性， 采用基于递推最小二乘法的多层前向神经网络算法， 并对它的条件参数进行了一般性的推导。该神经网络为3 层前向网络， 其隐含层神经元数为10 个， 输出层神经元数1 个。包括输出层与隐含层、输入层与隐含层间权的调整算法。用典型PID 算法和神经网络的控制算法分别仿真结果可见， 神经网络控制解决了压合过程纯滞后的非线性问题， 达到了预期的技术要求。
关 键 词 ： 最小二乘法； 神经网络；PID 算法
Abstract: Aiming at a series of control problems about putting a cone into thin canister, online and real time test of displacement sensor and pressure sensor, and the control of increasing pressure jar as executive machine, the multi-layer front neural network algorithm based on least square method (LSM) was applied to ensure stability and astringency of the system under the conditions of he system disturbed by the environment, and its some of condition parameters were deduced. There are 3 layers in multi-layer front neural network, there are 10 nodes in hide layer, and is 1 node in the output layer. The network contains adjusted algorithm of the weight between output and hide layer, and between input and hide layer. The emulation results of typical PID algorithm and neural network control algorithm show that the nonlinear problem for pressed process lag was resolved with the neural network control, the expected technical requirement was achieved.
Key Words: LSM; Neural network; PID algorithm
关 键 词 ： 最小二乘法； 神经网络；PID 算法
Abstract: Aiming at a series of control problems about putting a cone into thin canister, online and real time test of displacement sensor and pressure sensor, and the control of increasing pressure jar as executive machine, the multi-layer front neural network algorithm based on least square method (LSM) was applied to ensure stability and astringency of the system under the conditions of he system disturbed by the environment, and its some of condition parameters were deduced. There are 3 layers in multi-layer front neural network, there are 10 nodes in hide layer, and is 1 node in the output layer. The network contains adjusted algorithm of the weight between output and hide layer, and between input and hide layer. The emulation results of typical PID algorithm and neural network control algorithm show that the nonlinear problem for pressed process lag was resolved with the neural network control, the expected technical requirement was achieved.
Key Words: LSM; Neural network; PID algorithm
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