采用极值搜索方法，解决无人机紧密编队飞行中僚机所需动力最小化的问题。
The paper presents a design approach based on extremum seeking for minimum power demand of formation flight of Unmanned Aerial Vehicle.
针对无人机近距离编队飞行问题，采用极值搜索算法，解决其中僚机所需动力最小化的控制问题。
An approach based on extremum seeking algorithms for the minimum power demand of the wingman in formation flight of an UAV is presented.
针对无人机近距离编队飞行问题，采用极值搜索算法，解决其中僚机所需动力最小化的控制问题。
To solve the problem of unmanned aerial vehicle (UAV) formation flight, an approach of modeling close formation flight of UAV is presented.
数值实例表明，拥挤聚类模型在多极值搜索的数量、质量和精度上都优于拥挤模型与确定性拥挤模型。
Numerical results indicate that crowding clustering model is superior to both crowding model and deterministic crowding model in quantity, quality and accuracy of multi-optima searching.
针对如何解算n人非合作的动态博弈对策中的纳什均衡解问题，提出一种利用退火回归神经网络极值搜索算法解算纳什均衡解的方法。
An algorithm is proposed to solve the Nash equilibrium solution for ann-person noncooperative dynamic game by an annealing recurrent neural network for extremum seeking algorithm (ESA).
尽管如此，科学家们仍在进行搜索，寻找极值距离处的其它星系团。
Searches are nonetheless being carried out to find other galaxy clusters at extreme distances.
搜索多极值点问题是遗传算法研究领域内的一个新的方向。
Searching multi maximum is a new field in the research on genetic algorithms.
经典的粒子群优化算法是一个在连续的定义域内搜索数值函数极值的很有效的方法。
The classical Particle swarm optimization (PSO) algorithm is a powerful method to find the minimum of a numerical function, on a continuous definition domain.
由于应用了自适应搜索技术，局部搜索能够快速找到局部极值。
The adaptive search technique enables local search to head for local extrema quickly.
如果选取的权值范围距离目标极值区域很远，搜索空间越大，目标极值区域越窄，搜索时间就越长，训练速度就越缓慢。
If it is far from chosen range of weights to goal area, the search space is wider, goal area is narrower, search time is longer and training speed is slower.
在分析和阐述了离散概率密度演化理论和多维线性空间极值问题的基础上，给出了适合于该方案的非正则ldpc码的搜索算法和搜索结果。
By analyzing the discretized density evolution theory and the differential evolution algorithm, a method is proposed to search optimal irregular LDPC code for joint optimization of the system.
该算法的思想是采用混沌初始化进行改善个体质量和利用混沌扰动避免搜索过程陷入局部极值。
The basic principle of CPSO algorithm is that chaos initialization is adopted to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum.
该算法的思想是采用混沌初始化进行改善个体质量和利用混沌扰动避免搜索过程陷入局部极值。
The basic principle of CPSO algorithm is that chaos initialization is adopted to improve individual quality and chaos perturbation is utilized to avoid the search being trapped in local optimum.
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