Aiming at the difficulties existing in large-scale water pollution monitoring systems, a connectionist model based local optimization algorithm and its application are discussed in this paper.
针对水域污染监测数据融合系统中存在的困难，讨论了基于“连接”模型的局部优化算法及其应用。
The standard particle swarm optimization algorithm as a random global search algorithm, because of its rapid propagation in populations, easily into the local optimal solution.
标准的粒子群优化算法作为一种随机全局搜索算法，因其在种群中传播速度过快，易陷入局部最优解。
Aimed at particle swarm optimization (PSO) algorithm being easily trapped into local minima value in multimodal function, a rotating surface transformation (RST) method was proposed.
针对粒子群优化算法(PSO)应用于多极值点函数易陷入局部极小值，提出旋转曲面变换(RST)方法。
It makes a searching for all local optimization of the multimodal function that a PSO algorithm based on chaos sequence for multi-modal function optimization.
基于混沌序列的多峰函数微粒群寻优算法的目标就是找到多峰函数的所有局部优化峰值。
During adjustment process, a creative method named reverse adjustment was adopted, to prevent the algorithm from being got in local optimization trap.
该算法的基本思路是边构造边调整路径，在调整中采用了独创的逆向调整方法，避免算法陷入局部优化陷阱。
Chaotic mechanism is introduced to normal BP algorithm, and the problem of local limit value for network is solved using global moving characteristic of chaotic mechanism is weight optimization.
将混沌机制引入常规BP算法，利用混沌机制固有的全局游动，逃出权值优化过程中存在的局部极小点，解决了网络训练易陷入局部极小点的问题。
We present an improved genetic algorithm(GA) for topology optimization of a truss with discrete sizing and under local buckling constraints.
采用改进的遗传算法，求解了具有屈曲约束，尤其截面积是离散型的桁架拓扑优化。
The proposed algorithm incorporates the thoughts of heuristic algorithm, randomized algorithm and local optimization.
这种算法结合了启发式算法和随机化算法以及局部寻优的思想。
The contrast optimization algorithm is described as an optimization model via which the local optimal solution can be achieved.
本文将对比度最优自聚焦算法等效为一个求解局部最优解的优化模型。
For accelerating the algorithm convergence and avoiding the local optimization, an individual learning mechanism is often applied to generic algorithm to improve algorithm performance.
在遗传算法中引入个体学习机制能够提高算法的性能，避免算法收敛过慢或陷入局部最优。
The traditional fuzzy C-means (FCM) algorithm is an optimization algorithm based on gradient descending. it is sensitive to the initial condition and liable to be trapped in a local minimum.
传统的模糊c -均值(FCM)聚类是一种基于梯度下降的优化算法，该方法对初始化较敏感，且易陷入局部极小。
In solving global optimization, immune algorithm is usually weak in local search, while evolutionary diffusion optimization is weak in global search.
在求解全局优化问题时，通常免疫算法、进化扩散算法分别在局部搜索和全局搜索方面表现较弱。
Unlike other algorithms, this algorithm automatically recovers details in edited regions using volume preserving constraints and optimization technique rather than expensive local frames.
与传统编辑方法不同，该方法利用体积保持约束及优化技术，来自动恢复编辑区域中相应的细节，而无需引入非常损耗资源的局部标架。
Aiming at problem that Particle Swarm Optimization (PSO) algorithm falls into local optimum easily, this paper presents a PSO algorithm based on sub-region.
针对粒子群优化(PSO)算法在寻优时容易陷入局部最优的不足，提出一种基于子区域的PSO算法。
The particle swarm optimization(PSO) algorithm, is used to train neural network to solve the drawbacks of BP algorithms which is local minimum and slow convergence.
针对多层前馈网络的误差反传算法存在的收敛速度慢，且易陷入局部极小的缺点，提出了采用微粒群算法（PSO）训练多层前馈网络权值的方法。
These are all greedy which are static local optimal algorithm and have typical local optimization problem.
这些算法均属于贪心算法，存在典型的局部最小问题，是一种静态的局部最优算法。
Particle Swarm optimization (PSO) algorithm is a population-based global optimization algorithm, but it is easy to be trapped into local minima in optimizing multimodal function.
粒子群优化算法应用于多极值点函数优化时，存在陷入局部极小点和搜寻效率低的问题。
However, comparing with other intelligent algorithms, immune algorithm also owns some disadvantage, like premature convergence and poor ability in local optimization.
然而，类似于其它新型智能算法，人工免疫算法也存在一些不足，如存在早熟收敛、局部搜索能力不足等。
To solve the problem that particle swarm optimization algorithm is apt to trap in local optimum, a novel cooperative particle swarm optimization algorithm is proposed.
为了解决基本粒子群算法不易跳出局部最优的问题，提出了一种协同粒子群优化算法。
When an evolutionary algorithm is applied to global optimization problems, it may be trapped around the local optima of the objective function and has a low convergence-rate.
一个进化算法应用到全局优化问题的，它可能会陷入周围的局部最优解的目标函数，并具有较低的收敛速度。
A deterministic searching algorithm is embedded to improve the optimization performance and enhance the fast convergence during local search.
一个确定的搜索算法嵌入到改进？的优化性能，提高了收敛速度快，在本地搜索。
Finally, the whole microclimate model in greenhouse was set up with the powerful local-optimization of IBP algorithm.
最后，结合IBP算法强大的局部寻优能力完成整个温室小气候模型的建立。
In this paper, a local-optimization method oriented to frequently occurring faults is presented. A heuristic fault diagnosis algorithm with fewer probing operations is also discussed.
为此，我们面向高频度故障，提出了一个基于原测试流程的局部优化方法，并且还提出了一个以减少诊断所需探针操作次数为目标的启发式故障诊断算法。
A parallel optimization algorithm is proposed based on the idea of affinity maturation of immune cells in the germinal centers in order to achieve as many as possible local optimal solutions.
为此 ，研究了产品与过程设计活动之间的并行度优化问题 ，并建立了数学模型 ，给出了求解该问题的算法 ，其目的是使因延迟产品开发完成时间和不必要的设计修改而带来的成本尽可能最低。
A parallel optimization algorithm is proposed based on the idea of affinity maturation of immune cells in the germinal centers in order to achieve as many as possible local optimal solutions.
为此 ，研究了产品与过程设计活动之间的并行度优化问题 ，并建立了数学模型 ，给出了求解该问题的算法 ，其目的是使因延迟产品开发完成时间和不必要的设计修改而带来的成本尽可能最低。
应用推荐