Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ABSTRACT,  propose a model order reduction (MOR) method based on general orthogonal polynomials for K-por bilinear systems in the time domain. Constructing proper projection matrices by solving a series of linear equations, a reduced K-por bilinear system is produced, which preserves the original coupled structure. It can match several expansion coefficients of the original output. Then the error bound of our algorithm is also investigated. Moreover, the stability of the reduced system is discussed as ll. Finally, two numerical examples are provided to illustrate the effectiveness of our algorithm.
['Zhen-Zhong Qi', 'Yao-Lin Jiang', 'Zhi-Hua Xiao']
I am writing a research paper on a idea Time domain model order reduction using general orthogonal polynomials for K-power bilinear systems
[{"from": "human", "value": "I am writing a research paper on a idea Time domain model order reduction using general orthogonal polynomials for K-power bilinear systems"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ABSTRACT,  propose a model order reduction (MOR) method based on general orthogonal polynomials for K-por bilinear systems in the time domain. Constructing proper projection matrices by solving a series of linear equations, a reduced K-por bilinear system is produced, which preserves the original coupled structure. It can match several expansion coefficients of the original output. Then the error bound of our algorithm is also investigated. Moreover, the stability of the reduced system is discussed as ll. Finally, two numerical examples are provided to illustrate the effectiveness of our algorithm."}]