【作者】 章剑雄；

【导师】 冯浩；

【作者基本信息】 浙江工业大学 ， 控制理论与控制工程， 2003， 硕士

【摘要】 神经网络对于辨识和逼近复杂的非线性系统有优越的性能，已经在工程领域得到广泛而成功的应用。国内外许多学者采用神经网络来建立生产过程的模型，以达到预报或优化的目的。 本文介绍了近年来得到广泛应用的两种前馈神经网络：BP网络和RBF网络，并给出了学习算法。文中归纳了前馈型神经网络具有逼近任意非线性映射能力的理论性的结论。简单介绍了钢材退火生产工艺，给出了基于神经网络的钢材退火生产过程的建模与优化模型。最后，作了用BP网络和RBF网络逼近非线性函数的仿真研究，并作了比较。针对BP网络学习速度慢，容易陷入局部极小点等问题，给出了几种改进方法，取得了较好的效果。文中对前馈网络中的隐层神经元个数的确定，学习样本的选取以及神经网络的泛化能力作了较详细的讨论。最后，采用一种直接方法步长加速法来寻找最优值。更多还原

【Abstract】 Neural network is very effective to identify and approximate nonlinear dynamical system. It is applied widely and successfully in engineering area. Many researchers model process based neural network to predict or optimize yield.This paper presents two kinds of forward neural network: BP (Back-Propagation) and RBF (Radial Basis Function), and the learning algorithms are also introduced . Then the concept of steel annealing is expounded. And neural-based solution of modeling and optimization of steel annealing process is given. Nonlinear function approximation using BP neural network and RBF neural network is simulated. We propose some modifications of the back-propagation algorithm to speed up the convergence rate and increase the possibility to escape local minima. In this paper, we discuss detailedly about how to determine the number of hidden layer of neural network, about how to select learning samples, about generalization of neural network. At last, the algorithm to find the optimal value is given.更多还原