Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study  develop the theory lying behind a model-based dynamic recurrent neural network (MBDRNN) that has been previously used to improve the linearized models of nonlinear systems. The initial structure of the MBDRNN is based on the linearized system model. Afterwards, the MBDRNN is trained to represent the system's nonlinearities by adapting the ights of its nodes' activation functions using Back-Propagation. The MBDRNN is applied with analytical detail to an arbitrarily chosen Single-Input/Single-Output (SISO) second order nonlinear system, and comparisons are made beten the linearized and MBDRNN models, showing that the MBDRRN effectively improved the linearized model
['Marc Karam', 'Mohamed A. Zohdy']
I am writing a research paper on a idea Development of a Model-Based Dynamic Recurrent Neural Network for Modeling Nonlinear Systems
[{"from": "human", "value": "I am writing a research paper on a idea Development of a Model-Based Dynamic Recurrent Neural Network for Modeling Nonlinear Systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In this study  develop the theory lying behind a model-based dynamic recurrent neural network (MBDRNN) that has been previously used to improve the linearized models of nonlinear systems. The initial structure of the MBDRNN is based on the linearized system model. Afterwards, the MBDRNN is trained to represent the system's nonlinearities by adapting the ights of its nodes' activation functions using Back-Propagation. The MBDRNN is applied with analytical detail to an arbitrarily chosen Single-Input/Single-Output (SISO) second order nonlinear system, and comparisons are made beten the linearized and MBDRNN models, showing that the MBDRRN effectively improved the linearized model"}]