Titlebook: Engineering Applications of Soft Computing; Margarita-Arimatea Díaz-Cortés,Erik Cuevas,Raúl Ro Book 2017 Springer International Publishing

時間

書目名稱Engineering Applications of Soft Computing影響因子(影響力)




書目名稱Engineering Applications of Soft Computing影響因子(影響力)學(xué)科排名




書目名稱Engineering Applications of Soft Computing網(wǎng)絡(luò)公開度




書目名稱Engineering Applications of Soft Computing網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Engineering Applications of Soft Computing被引頻次




書目名稱Engineering Applications of Soft Computing被引頻次學(xué)科排名




書目名稱Engineering Applications of Soft Computing年度引用




書目名稱Engineering Applications of Soft Computing年度引用學(xué)科排名




書目名稱Engineering Applications of Soft Computing讀者反饋




書目名稱Engineering Applications of Soft Computing讀者反饋學(xué)科排名

滋養(yǎng)

開玩笑

Artificial Bee Colony Algorithm Applied to Multi-threshold Segmentation, an optimization problem. On the other Hand, ABC is a nature inspired algorithm based on the intelligent behaviour of honey-bees which has been successfully used to solve complex real life optimization problems.

修飾

排出

Lisa Bosman,Stephanie Fernhabernternal parameters. In this approach, the parameter estimation process is transformed into a multidimensional optimization problem where the internal parameters of the induction motor are considered as decision variables.

DOLT

DOLT

https://doi.org/10.1007/978-981-10-6725-9 an optimization problem. On the other Hand, ABC is a nature inspired algorithm based on the intelligent behaviour of honey-bees which has been successfully used to solve complex real life optimization problems.

甜食

Book 2017plain the basic ideas behind the proposed applications in an accessible format for readers who may not possess a background in some of the fields. Therefore, engineers or practitioners who are not familiar with Soft Computing methods will appreciate that the techniques discussed go beyond simple the

爭吵加

大看臺

Multi-threshold Segmentation Using Learning Automata, image is approximated through a Gaussian mixture model whose parameters are calculated using the LA algorithm. Each Gaussian function approximating the histogram represents a pixel class and therefore a thresholding point. Experimental results show fast convergence of the method, avoiding the typical sensitivity to initial conditions.