Study Of Orthogonal Recurrent Neural Network And Its Application In Industrial Control

Society developments are increasing the complexity of many real tasks, which makes the traditional control theories limited. It is no doubt that more and more appearances of advanced tools such as neural networks, genetic algorithm and fuzzy logic can cause development of control theories. To overcome the drawback of existing neural network models in control and modeling of dynamic nonlinear systems, a novel neural network model known as Dynamic Orthogonal Polynomial Basis Neural Network (DOPBNN) is presented in this thesis. The modeling and control strategies and methodologies based on the DOPBNN are discussed in this thesis.The main contributions of this thesis are as follows:1) Up to date methods, applications, researches and drawbacks of existing neural networks in the cybernetics are summarized firstly. It becomes a good starting point of following research work.2) Based on the combination of orthogonal polynomial theory and the existent recurrent neural network structure, a novel neural network model DOPBNN is proposed. It is proved that given enough number of neuron in the middle layer, a DOPBNN can approximate any dynamical system to any degree of accuracy.3) Some important inequalities about Legendre polynomial are proposed and proved. Based on these inequalities, the sufficientconditions of globally exponential stability criteria is proposed4) The application of DOPBNN to modeling dynamic nonlinear systems is researched in detail. The update laws of weights of DOPBNN derived from the Lyapunov method ensure the convergency of the identification error and weights. By using the singular perturbation method, it is proved that if singular perturbation satisfies some conditions the convergency is ensured even there exist...