应用蒙特卡罗方法，将利润、成本和机会损失的统计量作为适应值函数。
We adopt Monte Carlo simulation method, which makes the statistics amount of gross profit, cost and opportunity loss as fitness function.
在计算染色个体的适应值函数时充分考虑了飞行器的动力约束以及规避障碍能力。
Both the dynamic constraint and the ability eluding obstacles of aircraft are considered enough in the computation of fitness function.
采用遗传算法进行节点优化编号，提出了适合节点优化编号的遗传编码和适应值函数。
To apply genetic algorithms to node ordering, a novel genetic coding and fitness function suitable to node ordering are proposed.
我们给出了DNA计算中序列设计的支持系统：计算由多个评价指标的线性和组成的适应值函数的最小值。
We develop support system for sequence design in DNA computing, which minimizes the evaluation function calculated as the linear sum of the plural evaluation terms.
采用演化计算方法，对初步得出的文档矢量做进一步的优化处理，在保证原文含义的基础上，找出最能反映样例文档内容，又比较简洁的特征矢量，并提出新的适应值函数。
Under the conditions of preserving the meaning of the sample document, a feature vector which can best reflect the content of the sample document and is comparatively brief is found.
针对BSCB模型速度很慢的缺点，提出了结合扩散率函数的选择性自适应插值算法。
Because BSCB models main drawback is slow calculation, this paper proposes selective adaptive interpolation algorithm based on diffusivity function.
因此，本文提出的自适应有理函数插值方法可以对大量采样数据进行插值运算而不会遇到奇异性问题。
So the adaptive rational function interpolation method can process a large number of sampling data for obtaining a rational interpolation without suffering singularity problems.
PSO的位置向量对应模糊神经网络的权值向量，而PSO的适应函数对应模糊神经网络的目标函数，然后，通过演化PSO达到训练模糊神经网络的目的。
Position vector of a PSO is wight vector of trained FNN, and fitness function of the PSO is object function of trained FNN, the FNN is then trained by evolving the PSO.
利用凝聚函数求出个体的约束违背值，在选择中不仅考虑适应度值而且考虑约束违背值，使有潜力的个体优先被选择。
This reproduction considers both the fitness values and constraint function values calculated by surrogate function, which ensures potential designs to be chosen preferentially.
提出了一种自适应变步长恒模盲均衡算法，利用剩余误差信号的自相关函数估计值作为控制步长的因子来自适应改变步长的大小，克服了恒模算法存在的固有缺陷。
A new variable step-size CMA blind equalization algorithm is introduced to conquer the defects of CMA, in which the step size is controlled by the estimation of error signal's autocorrelation.
采用RBF神经网络逼近系统未知的非线性函数，引入滑模误差对其权值进行在线自适应调整，改善动态性能。
RBF neural network is proposed to approximate unknown nonlinear function. Sliding mode error is used to adaptively tune its weights online. Dynamics performance is improved.
采用RBF神经网络逼近系统未知的非线性函数，引入滑模误差对其权值进行在线自适应调整，改善动态性能。
RBF neural network is proposed to approximate unknown nonlinear function. Sliding mode error is used to adaptively tune its weights online. Dynamics performance is improved.
应用推荐