粗集方法可直接处理歼击机的可测输出信号，不需要对象的数学模型和相关先验知识。
The proposed method dealt with measurable signal of the aircraft, mathematical model and no relevant transcendental knowledge was needed.
这种方法可直接处理系统的可测输出信号，不需要对象的数学模型和相关先验知识，具有较高的实用性。
This method directly deals with measurable signal of the system, mathematical modal and relevant transcendental knowledge isn't needed, so it is more practical than other methods.
这个方法通过对系统进行主动干扰来获得建模的先验知识，然后基于贝叶斯网络构造方法，对分布式应用的组件间关系建立依赖性模型。
The method relies on active perturbation to the system to obtain prior knowledge for building the model, and then constructs the dependency model based on the methods of building Bayesian networks.
仿真结果表明，在没有被控对象先验知识的情况下，利用该方法能准确地建立连续非线性系统的逆模型。
Simulation results show that the presented method can accurately construct the inverse dynamic model of the continuous nonlinear system even without prior knowledge about the controlled plant.
基于云模型的分类算法多采用云变换和泛概念树方法，存在分类结果与先验知识不一致的问题。
The classification based on cloud model always USES cloud transform and Pan-Concept-Tree, it brings on inconsistency between the result and prior knowledge.
本研究提出了一种新的基于先验知识的弹性配准算法，首次把马尔可夫模型应用于图像的弹性配准方面。
The algorithm was constructed by integrating the elastic registration algorithm based on B-spline and the apriori knowledge of the deformation field into a MRF model.
传统的智能模型库系统对先验知识依赖性很强，难以实现真正意义上的智能化。
Traditional intelligent model base system depends on transcendental knowledge, and it is difficult to authentically implement intelligentization.
核密度估计方法从数据样本本身出发研究数据分布特征，不利用有关数据分布的先验知识，避免了模型估计和参数估计的主观影响。
The kernel estimation method analyzes the data distribution by not using the prior knowledge of data distribution. This method avoids the impaction of model and parameters estimation.
自适应逆控制需要很少的先验知识，不需要知道被控对象的数学模型，就可以设计出性能良好的自适应逆控制系统。
Adaptive inverse control can be used to design well-performed adaptive inverse control system with little prior experience and without knowing the mathematic model of the controlled device.
进化计算模型不需要太多领域知识和建立先验模型，在处理复杂的数据挖掘问题中得到了广泛的青睐。
Among various data mining technologies, Evolution Computing has been widely applied in complex problem handling because it requires little field knowledge and no prior model.
在此基础上，笔者充分利用先验条件和专家知识来确定飞机模型上的同名点，初步得到了飞机模型的轮廓侧视图。
On this basis, the priori conditions and the expert knowledge have been used for seeking the corresponding points. Finally, a model side outline is obtained.
但是，有的知识发现技术建立的模型要么比较复杂，要么需要一定的先验知识、具有主观性。
However, some model based on KDD technology is more complicated or needs certain field knowledge, which is subjective.
本文在对连续自适应均值漂移算法深入分析的基础上，提出利用目标外观信息的先验知识，对其建立多个颜色分布模型。
In this paper, we research the CamShift tracker in detail and then proposed to build multiple color-distribution-model for the target accordng to prior knowledge of the objects appearance.
而自适应逆控制需要很少的先验知识，不需要知道被控对象的数学模型，就可以设计出性能优良的自适应逆控制系统。
We can only make a tradeoff between them. However adaptive inverse control can deal with the two problems separately. It doesn't need too much prior knowledge.
贝叶斯网络的学习是数据挖掘中非常重要的一个环节，是将先验知识和模型评价融入训练数据，获得数据中隐藏的拓扑结构和参数的过程。
The learning of Bayesian Networks is an important tache, which combines training data with prior knowledge and model evaluation to acquire the structure hidden in data and parameters.
灰色局势决策方法是一种不依赖于过程模型先验知识的目标决策方法。
In view of the uncertainty of rolling delay time, the method of multi-objective gray state decision making is applied.
这种模型基编码方法不需要图象的先验知识，模型建立及方案实现较为简单。
By this scheme, neither any priori knowledge of an image is needed nor too much complicated techniques must be applied for model's construction or realization.
该 算法利用查询模型计算各种特征向量的先验 知识，然后动态地选择描述能力较强的特征向量计算模型之间的相似度距离。
The query model first calculates the prior knowledge of the feature vectors and then dynamically chooses the feature vector with the best description.
该方法不要求的先验知识的控制增益的符号和的上界和下界的先验已知的死区模型参数。
The approach does not require apriori knowledge of the sign of the control gain and the upper bound and lower bound of dead zone model parameter to be known apriori.
该方法不要求的先验知识的控制增益的符号和的上界和下界的先验已知的死区模型参数。
The approach does not require apriori knowledge of the sign of the control gain and the upper bound and lower bound of dead zone model parameter to be known apriori.
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