SpeedADRC_CurrentPI

matlab教程 不过 大家看看 比较实用哦!内容很详细()

Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimization algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.

MATLAB java web ,这是一个调用MATLAB 算法的好例子，可以用来开发自己的算法(MATLAB java web)

计算分形维数，里面提供了分线法、盒维法和三角棱柱法。(Calculate the fractal dimension, which provides a sub-line method, box dimension method and the triangular prism method.)

基于栅格划分的环境中,研究了机器人路径规划问题中蚁群系统的“外激素”表示及更新方式,并将遗传算法的交叉操作结合到蚁群系统的路径寻优过程中,提高了蚁群系统的路径寻优能力,为蚁群算法的应用提供了一种新的探索。(Raster-based division of the environment, to study the issue of robot path planning in the ant colony system of )

说明：  matlab的函数目录，，作为初学者的函数字典，，很有参考价值(matlab function directory, as a function of beginners dictionary, a good reference)

matlab simulation of three phase induction motor operation with unbalanced supply system

fingerprint recognition system

(7,3)线性分组码的译码过程和产生过程,此程序为信息课的作业((7,3) linear block code decoding process and the production process, this process work for the information class)

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima–media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale–space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen–intima