建立燃气轮机数学模型是船舶动力及其控制系统研制的重要手段，建模的关键在于提高精度和收敛速度。

The mathematic model of gas turbine engine is important for the development of ship power and its control system. The key of modeling is the precision and convergence.

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采用一类全局收敛的连续模拟退火算法完成了控制器参数的优化设计。

A continuous simulated annealing algorithm with global convergence ability is applied to solve this controller parameters optimization design problem.

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针对传统神经网络用于复杂过程系统的控制时难于收敛的问题，文章提出了基于混合建模的模块化的神经网络模型。

Against difficult convergence problem when the traditional neural networks are applied to complex process system, a hybrid expert neural networks model is brought forward.

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