基于自构建RBF神经网络的内模控制

针对复杂的非线性被控过程，本文提出一种基于自构建RBF神经网络的内模控制方法。该方法中，神经网络的自构建学习算法包括结构学习和参数学习。结构学习采用最近邻聚类法使网络能够自适应地在线增加和删减神经元以达到理想的网络结构。神经网络的参数学习采用梯度下降法。将该神经网络用于内模控制，使得辨识被控远程内部模型和控制器模型的神经网络的神经元个数可以根据激励强度动态改变，进而改善了控制系统的动态性能和鲁棒性。仿真结果表明了所提方法的有效性。

Internal Model Control Based on Self-constructing RBF Neural Network

A novel internal model control （IMC）algorithm based on self-constructing radial basis function （RBF） neural network （NN） is proposed for the complex nonlinear process. The self-constructing learning algorithm is composed of structure learning and parameters learning. The closet clustering algorithm is used in the NN structure learning, and it makes the network determine the nodes dynamically to achieve optimal network structure. The gra- dient descending method is adopted in the parameters learning. Then,this NN is applied to IMC. The neuron num- ber of the networks that identifies the internal model of the controlled process and the model of controller can change according to the incentive intensity, and the dynamic performance and robustness of the system could be im- proved. The simulation shows that the proposed method is effective.