由于粗神经网络的误差传递函数不可微，所以采用遗传算法来训练粗神经网络。
Because the error transfer function of rough neural network is not differentiable, genetic algorithms are applied for training the network.
说明用神经网络模型描述微囊制作参数与性能之间的关系，用遗传算法优化微囊制作工艺参数，能设计出性能最佳的微囊制作工艺参数。
The optimum process parameters could be obtained using ANN model to describe relationships between process parameters and performance and GA to optimize process parameters.
为了提高运算效率，采用微种群遗传算法来加速收敛性。
To improve computational efficacy, a micro-genetic algorithm was employed to accelerate convergence.
遗传算法直接对结构对象进行操作，不存在函数可微性和连续性的限定，具有全局性，鲁棒性和隐并行性等优越性。
To solve problem, GA deals with structural object directly without any requirements of differentiability and continuation of function; it's global, robust and parallel in working mode.
而遗传算法不需要待优化函数具有连续可微性，并具有很强的通用性和隐含并行性。
While the GA does not require that the optimized functions possess continuum differentiability, GA is proposed to optimize the parameters of fuzzy controller.
而遗传算法不需要待优化函数具有连续可微性，并具有很强的通用性和隐含并行性。
While the GA does not require that the optimized functions possess continuum differentiability, GA is proposed to optimize the parameters of fuzzy controller.
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