A nonlinear internal model control (IMC) strategy based on radial basis function (RBF) network models was proposed for bulk polymerization of styrene.
文中研究基于径向基(RBF)神经网络算法的内模控制策略在苯乙烯本体聚合反应相对分子质量分布控制领域的应用。
This paper introduces the radial basis function (RBF) network in the seismic data processing, and realizes the inserting data in seismic data processing with function approximation method.
该文将径向基函数网络引入地震数据处理中，实现了函数逼近法地震数据的插值处理，在实际地震数据处理中取得了较好的应用效果。
The structure and features of radial basis function (RBF) network are introduced.
介绍了径向基函数(RBF)神经网络的结构和特点。
A two-level learning method combining improved immune algorithm and least square method was proposed to design a radial basis function (RBF) network.
结合改进的免疫算法和最小二乘法，提出了一种设计径向基函数(RBF)网络的两级学习方法。
Radial basis function (RBF) network have unique advantages in control applications due to its features of simple topological structure, quick convergence speed and no local minima.
径向基函数(RBF)神经网络由于其结构简单、收敛速度快、无局部极小等特点使其在控制中的应用有着独特的优势。
Defects are classified by radial basis function (RBF) network and features selected.
利用径向基函数神经网络和选择的特征值对缺陷进行分类。
Defects are classified by radial basis function (RBF) network and features selected.
利用径向基函数神经网络和选择的特征值对缺陷进行分类。
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