资源列表
SOM
- som神经网络的数据分类--柴油机故障诊断-som neural network data classification- Diesel Engine Fault Diagnosis
Neural-network-
- 神经网络的前馈控制仿真，模型是空间漂浮基机器人手臂的动力学方程-The neural network feedforward control simulation model, the model is floating space, the dynamic equations of a robotic arm
five-kind-BP-for-traffic-prediction
- 采用五种不同的局部算法对BP网络进行改进，预测交通流量，根据不同的数据特征可以进行不同的选择-Using five different local networks to improve the BP algorithm to predict traffic flow, according to different data characteristics can be different choices
CEP
- 基于高斯变异的进化规划用于解决非线性无约束函数问题-The Evolutionary programming for sloving the nonlinear unconstrained function by adapting the Gauss mutation
DVHOPS
- 基于粒子群和基于PSO和基于遗传算法的DV-hop算法的定位，有比较好的效果-Particle swarm based PSO, based on genetic algorithm DV-hop positioning algorithm, a relatively good results
BPPID
- 实现了神经网络PID 控制效果与传统PID比较，更加精确-Implement the neural network PID control performance compared with traditional PID, more accurate
ants
- 求解TSP 优化公交线网 改进的蚁群算法 可运行，比普通蚁群算法效果较好-Public transportation network optimization for TSP improved ant colony algorithm can be run better than the average ACO
meanS3VM
- MissSVM是一揽子解决多实例使用半监督支持向量机的学习问题。MissSVM目的是显示，如果假设IID实例，多实例学习可以作为一个半监督学习的特殊情况来看，可能会合并成半的领域和多实例学习领域监督学习。 因此，未来的多实例学习研究应只承担IID袋，避免IID实例假设。 -MissSVM package solution is to use multi-instance semi-supervised support vector machine learning problems. MissS
GRNN
- 本算法是matlab神经网络工具箱提供的函数建立了利用GRNN和RNN分别建立各个属性及属性组合与鸢尾花种类间的识别模型。-The algorithm is matlab neural network toolbox provides functions to establish the use of GRNN and RNN were established with various attributes and attribute combinations between species o
mamcpso
- 粒子群算法与多智能体系统相结合所编写的算法-Particle swarm optimization with a combination of multi-agent system algorithm written
LS-SVMlab-GA
- 用遗传算法优化的最小二乘支持向量机，简单易用，可移植性强，通用性好，程序特别的全。-Using genetic algorithm least squares support vector machine, easy to use, portability, common good, especially the whole program.
Chapter-13
- 模糊神经网络解耦算法（遗传算法优化）FNN对非线性多变量系统的解耦方法-Fuzzy neural network decoupling algorithm (GA optimization)FNN for nonlinear multivariable system decoupling method