搜索资源列表
estekhraj-vijegi
- Design a feedforward network is u dehaze algorithms review path planning using GA and ACO... The neural network adaboost stron TS neural network m files, fast c bp neural network, written with C Bayes net and memory based learni
nnafuzz
- Neural Network and Fuzzy Logic Applications in C/C++. Book Disk. Turbo Vision UI, Feed- forward network files, Genetic algorithms, Chaotic dynamics, Financial data modeling, Broom dynamics, Hopfield network, Fuzzy logic control & Genet
Ensemble-Classifier-for-Concept-Drift-Data-Stream
- In this era an emerging filed in the data mining is data stream mining. The current research technique of the data stream is classification which mainly focuses on concept drift data. In mining drift data with the single classifier is not sufficient
gaandassigmentmatlab
- 基于遗传算法的模糊指派程序matlab源代码-based on genetic algorithm Fuzzy Assignment
classificiation-algorithm-overview
- 机器学习领域经典分类算法综述，包括Decision Tree（ＩＤ３、C4.5(C5.0)、CART、PUBLIC、SLIQ和SPRINT算法），三种典型贝叶斯分类器（朴素贝叶斯算法、TAN算法、贝叶斯网络分类器），k-近邻 、 基于数据库技术的分类算法（ MIND算法、GAC-RDB算法），基于关联规则（CBA:Classification Based on Association Rule）的分类（Apriori算法），支持向量机分类，基于软计算的分类方法（粗糙集（rough set）、遗传
num11
- here I insert source code forSelecting Fuzzy If-Then Rules for Classification Problems Using Genetic Algorithms.
ISKE2007-Zhang_Xiaofei.pdf
- Fuzzy Control of Fin Stabilizer at Zero Speed Based on Improved Genetic Algorithm
BGA_FUZZY-controler
- Binary genetic algorithm with fuzzy controller algorithm Matlab code
gabpf
- 模糊神经网络和遗传算法结合的船舶火灾探测(模糊神经网络和遗传算法)-Fire ships based on fuzzy neural network and genetic algorithm (detection of fuzzy neural network and genetic algorithm)
GUI-based-on-Matlab
- 遗传算法改进的模糊C-均值聚类MATLAB源码。用单纯形法来做的-Improved genetic algorithm fuzzy C- means clustering MATLAB source. To do with the simplex method
shenjingwanglouzhongheyingyong
- 神经网络综合应用 主要有BP神经网络的应用、PID神经网络的控制、遗传算法优化神经网络、模糊神经网络、概率神经网络-Neural network integrated applications are mainly controlled BP neural network applications, PID neural networks, genetic algorithm optimization of neural networks, fuzzy neural network, proba
APSO-GA-ACO-TSP
- 综合粒子群和蚁群算法，再利用遗传算法中交叉编译算子；再引入模糊技术，形成模糊自适应粒子群和蚁群混合算法，求解TSP问题-Integrated particle swarm and ant colony algorithm, and then genetic algorithm is used to cross compile operator then introduced the fuzzy technology, forming a fuzzy adaptive particle swar
herraiz_icsoft2011
- Optimal power flow (OPF) is one of the nonlinear problems of power system. The various algorithms for solving optimal power flow problem are found in the literature. The genetic algorithm (GA) based solution techniques are found to be most suitab
AForge.NET
- AForge.NET是一个专门为开发者和研究者基于C#框架设计的，他包括计算机视觉与人工智能，图像处理，神经网络，遗传算法，机器学习，模糊系统，机器人控制等领域。-AForge.NET is a specialized developer and researcher based C# framework designed, he included computer vision and artificial intelligence, image processing, neural netw
Intelligent-control-of-courseware
- 自能控制介绍包括模糊控制，神经网络控制，遗传算法，自持向量机，智能控制算法，PID神经网络控制-Self introduction can control including fuzzy control, neural network control, genetic algorithm, self-sustaining vector machine, intelligent control algorithm, PID neural network control
fcmMATLAB
- 模糊C-均值算法容易收敛于局部极小点，为了克服该缺点，将遗传算法应用于模糊C-均值算法(FCM)的优化计算中，由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最终的分类结果。-Fuzzy C- means algorithm is easy to converge to a local minimum point, in order to overcome this drawback, the genetic algorithm is applied to fuzzy C- me
SGALAB1003beta5008_agriculture
- 带有模糊逻辑控制的多目标遗传算法,包括19个m文件和9个说明文档-Multi-objective genetic algorithm with fuzzy logic control，which include nineteen .m documents and nine guiding documents
GAFCM
- matlab编写的遗传算法优化的模糊C均值聚类算法-genetic algorithm optimize Fuzzy C means algorithm
gafcm
- 这是遗传算法改进的模糊C-均值聚类MATLAB源代码,由遗传算法得到初始聚类中心，再使用标准的模糊C-均值聚类算法得到最终的分类结果。-This is the MATLAB source code of the improved fuzzy c-means clustering(FCM) based on the genetic algorithm(GA).The initial cluster centers are otained through GA,and the final clust
cluster
- 本程序介绍了聚类分析的各种算法，包括层次、动态、模糊和遗传算法，对数值进行模式识别。-This procedure describes the various clustering algorithms, including the level of dynamic, fuzzy and genetic algorithms, pattern recognition value.