一种新的连续时间系统混合进化算法

Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228) Pub Date : 2001-12-04 DOI:10.1109/CDC.2001.980988

G. L. Santosuosso

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引用次数: 0

摘要

针对实时模拟优化问题，提出了一种新的进化算法原子隐喻优化策略(AMOS)。这种新的进化算法与基于Lyapunov稳定性理论的连续时间自适应观测器算法相结合，该算法是针对具有线性参数化的近似函数类而开发的。该组合混合算法通过对系统动力学进行非线性参数化神经逼近，应用于连续时间非线性系统的在线建模。

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AMOS-a new hybrid evolutionary algorithm for continuous time systems

A novel evolutionary algorithm called atomic metaphor optimization strategy (AMOS) is proposed, which is designed for real-time analog optimization problems. This new evolutionary algorithm is integrated with the continuous time adaptive observer algorithm based on the Lyapunov stability theory, developed for classes of approximating functions with linear parametrization. The combined hybrid algorithm is applied to the online modeling of continuous-time nonlinear systems, via a nonlinearly parametrized neural approximation of the system dynamics.

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Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228)

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