搜索资源列表
DEELMNN
- 利用差分演化优化极限学习机神经网络的matlab源代码，涉及2个matlab程序-evolutionary extreme learning machine, differential evolution
FOA-ELM
- 算法思想是：1） 根据果蝇优化算法得到极速学习机隐层神经元的数目；2） 依据得到的隐层神经元数目和极限学习机的方法对训练样本和测试样本进行训练学习。只要打开fruitfly_elm.m文件运行即可，可以换数据集 -Algorithm idea is: 1) according to the number of flies speed machine learning algorithm to obtain the hidden layer neurons optimization Method
BA_ELM
- 用蝙蝠算法（DE）对极限学习机（ELM）的输入权值和偏执进行进行优化，其诊断精度有明显提升。-With bat algorithm (DE) on Extreme Learning Machine (ELM) input weights and paranoia were optimized diagnostic accuracy has improved significantly.
DE_OS-ELM
- 用差分算法对在线惯序极限学习机的输入权值和偏置进行优化（本程序只限激励函数为sig），其学习时间比极限学习机分批进行学习总共的时间有所提升，诊断精度也提高了。-Algorithm using differential input weights and the bias line Extreme Learning Machine sequencer used to optimize (this program only excitation function for sig), its lear
oo-ELM
- 优化的基线学习机，收敛速度快，可用于分类和拟合-The baseline learning machine optimization, fast convergence speed, and can be used for classification and curve fitting
NSGA2-ELM
- 以NSGA2算法作为学习算法优化ELM神经网络的权值，满足误差小、权值范围小的双目标(NSGA2 algorithm is used as a learning algorithm to optimize the weights of ELM neural network, and it meets the double objective with small error and small weight range)
PSO_ELM
- 运用粒子群算法对ELM算法进行优化，以达到算法的最优性。(Particle swarm optimization (PSO) is applied to optimize the ELM algorithm to achieve the optimality of the algorithm.)
BA_ELM
- 蝙蝠优化的极限学习机,提升极限学习机的效率(BA-ELM to improve effection of ELM)
粒子群优化
- 基于粒子群优化算法的ELM，很稳，自己写的亲测可用(ELM based on particle swarm optimization algorithm)
GA-ELM
- 遗传算法优化的极限学习机模型 采用水仙花基本特征数据集 效果比单纯的ELM模型要好(The effect of using daffodils basic feature data set in the extreme learning machine model optimized by genetic algorithm is better than that of ELM model only.)
全局群智能优化算法改进ELM
- 利用全局优化算法改进群智能算法从而改进ELM(Global group intelligence optimization algorithm improves ELM)
PSO-ELM
- PSO-ELM 粒子群算法优化极限学习机(PSO-ELM Particle swarm optimization for extreme learning machine)
基于PCA+PSO-ELM的工程费用估计
- 利用主成分分析法结合粒子群（PSO）优化极限学习机(ELM)进行工程费用估计预测(In this paper, principal component analysis (PCA) combined with particle swarm optimization (PSO) optimization extreme learning machine (ELM) is used to estimate and forecast engineering cost)