Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network ight adaptation law is derived from Lyapunov stability analysis and the connection beten convergence of the network ight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach.
['Quanmin Zhu', 'Lingzhong Guo']
I am writing a research paper on a idea Stable adaptive neurocontrol for nonlinear discrete-time systems
[{"from": "human", "value": "I am writing a research paper on a idea Stable adaptive neurocontrol for nonlinear discrete-time systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  presents a novel approach in designing neural network based adaptive controllers for a class of nonlinear discrete-time systems. This type of controllers has its simplicity in parallelism to linear generalized minimum variance (GMV) controller design and efficiency to deal with complex nonlinear dynamics. A recurrent neural network is introduced as a bridge to compensation simplify controller design procedure and efficiently to deal with nonlinearity. The network ight adaptation law is derived from Lyapunov stability analysis and the connection beten convergence of the network ight and the reconstruction error of the network is established. A theorem is presented for the conditions of the stability of the closed-loop systems. Two simulation examples are provided to demonstrate the efficiency of the approach."}]