以NGA训练的模糊网络模型，在收敛速度、寻优能力和辨别效果方面均优于BP、GA算法下的模型，符合缺陷识别的工程需要。
The FNN constituted by NGA, whose convergence rate, optimizing capability and distinguish effect are superior to those of GA and BP, is accorded with project need of discriminating.
传统的优化方法充分利用了目标问题的信息，局部寻优能力较强，收敛速度较快，但又会陷入局部最优的陷阱。
Traditional optimization methods use much more information of the target problem, so their convergence speed is much better, and the ability of finding local optimal is better.
比照传统遗传算法与生物界进化过程，分析了引起传统遗传算法收敛速度慢和寻优效率低的两个原因。
By contrasting the traditional genetic algorithms (TGA) with the biologic evolution, two kinds of reasons that the convergence speed and searching efficiency in TGA are both lower are concluded.
本文通过引入局部增强算子，使种群中的部分个体在当前最优个体附近寻优，以加快算法的收敛速度。
To improve the efficiency of the algorithm, the local enhanced operator is proposed to make some individuals of the population search around the current best individual.
优化算例表明，这种方法具有较强的寻优能力和理想的收敛速度。
Optimization examples show this method has strong searching ability and ideal convergence.
测试结果表明，新算法具有更快的收敛速度和更强的全局寻优能力。
Test results show that the new algorithm has faster convergence speed and better global optimization ability in the multi-dimensional space.
结果表明改进后的BP算法减少了迭代次数，提高了寻优的收敛速度。
The MATLAB simulation shows the improved weighted algorithm is of more efficien than conventional BP algorithm.
为了提高差分进化算法(DEA)的收敛速度和寻优精度，提出了一种改进的差分进化算法。
To improve the optimum speed and optimization accuracy of Differential Evolution Algorithm (DEA), an improved DEA was proposed.
实验结果表明，新算法收敛速度快，寻优能力强，能很好地求解约束优化问题。
Simulation results show that the proposed approach has fast convergence and good optimization ability, and is suitable for solving constrained optimization problems.
通过种群优化分类，使搜索半径不断缩小，实现了自适应连续优化搜索，较大提高了网络优化收敛速度、解的精度及全域寻优能力。
The Genetic Algorithms are optimized by the clustering analysis. The search area is reduced continuously by classifying the population. The continuously adaptive optimizing search is implememented.
优点：收敛速度快，具有全局寻优能力，而且编程简单，易于推广使用。
Advantages: fast convergence with global optimization capability and programming simple and easy to use.
优点：收敛速度快，具有全局寻优能力，而且编程简单，易于推广使用。
Advantages: fast convergence with global optimization capability and programming simple and easy to use.
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