Asymptotic minimization of the approximation error of competitive associative nets and its application to temperature control of RCA cleaning solutions
Asymptotic minimization of the approximation error of competitive associative nets and its application to temperature control of RCA cleaning solutions
复制标题
竞争关联网逼近误差的渐近最小化及其在RCA清洗液温度控制中的应用
DOI：
10.1109/iconip.2002.1199004
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发表时间：
2002
期刊：
影响因子：
--
通讯作者：
K. Itoh
中科院分区：
文献类型：
--
作者：
S. Kurogi;H. Sakamoto;H. Nobutomo;Y. Fuchikawa;T. Nishida;M. Mimata;K. Itoh
竞争联想网络，称为CAN 2 -2，提出了学习近似的时变动态的植物，以控制植物。虽然学习方法已被证明是有效的，在以前的研究中，它使用的梯度方法涉及局部极小值问题。为了克服这些问题，我们在这里考虑一个渐近的情况下，其中的网络的单元的数量是非常大的，并显示的均方误差的CAN 2 -2在近似时变函数减少，并最小化作为单位的数量增加时，单位的射击数量相等。接下来，我们嵌入的条件，使点火数到学习算法的CAN 2 -2，然后检查传统的模型切换预测控制器使用修改后的CAN 2 -2的RCA解决方案的温度控制清洗硅晶片暴露的放热非线性和时变化学反应。实验结果表明，该方法具有较好的学习性能。
The competitive associative net, called CAN2-2, is presented for learning to approximate time-varying dynamics of a plant in order to control the plant. Although the learning method has been shown effective in the previous studies, it uses the gradient method involving local minima problems. To overcome the problems, we here consider an asymptotic situation, where the number of units of the net is very large, and show that the mean square error of the CAN2-2 in approximating time-varying function decreases and is minimized as the number of units increases when the firing numbers of the units are equated. Next, we embed the condition for equating the firing numbers into the learning algorithm of the CAN2-2, and then examine the conventional model switching predictive controller using the modified CAN2-2 in temperature control of the RCA solutions for cleaning silicon wafers which expose the exothermic nonlinear and time-varying chemical reactions. The result confirms that the present method has better learning properties than the conventional one.
使用批量学习竞争关联网络对 RCA 清洁解决方案进行温度控制
DOI：
--
发表时间：
2004
期刊：
Proceedings of the 8th World Multi-Conference on Systems, Cybernetics and Informatics (SCI2004) Vol. V
影响因子：
--
作者：
S.Kurogi;N.Araki;H.Miyamoto;Y.Fuchikawa;T.Nishida;M.Mimata;K.Itoh
通讯作者：
K.Itoh