The dynamic optimization of learning parameters can adjust learning parameters dynamically and select optimal learning parameters.
随后，动态优化学习参数算法动态地调整和选取优化的学习参数。
Before the optimization modeling, the scaling step technique is used to adjust the structural behavior, and the stability of computational convergence is ensured.
在每一步迭代过程建立优化模型之前，采用射线步技术，调整了结构的性态，保证了收敛的稳定性。
And optimization technology is used to adjust the ventilation structure dimensions, so that the temperature of each part in the machine can be well distributed.
并利用优化技术调整结构尺寸，使各部件中温度分布趋于均匀。
And based on operational changes, system optimization, and other factors, the model to continuously adjust.
并依据业务更改、系统优化等多方面因素，对模型进行不断调整。
Finally, the joint stations to optimize the process parameters, analysis of the furnace, pump and other major equipment problems and optimization methods to adjust.
最后对联合站内的处理工艺参数进行优化，分析了加热炉、泵等主要设备存在的问题和优化调整方式。
Universal optimization for adjustable parameters of fuzzy based function is realized by using GA and finding adjust law of parameters in general designing adaptive controller is replaced.
通过一种新学习算法的导出，并结合模糊逻辑系统中的模糊基函数，给出了一种带有通用规则库的模糊滑模自适应控制器。
Universal optimization for adjustable parameters of fuzzy based function is realized by using GA and finding adjust law of parameters in general designing adaptive controller is replaced.
通过一种新学习算法的导出，并结合模糊逻辑系统中的模糊基函数，给出了一种带有通用规则库的模糊滑模自适应控制器。
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