搜索资源列表
OS-ELM
- 模糊神经网络实现函数逼近与分类，实现模糊规则的提取。-Fuzzy neural network function approximation and classification, to achieve the extraction of fuzzy rules.
os-elm
- 主要是基于extreme learning machine的改进算法，结合了增量学习-It’s mainly based on the extreme learning machine improvement algorithm, unified the increase study
os-elm
- 实现增量极限学习机，在线学习的方法等等，实现了在线连续学习功能-code written in matlab
OS-ELM
- OS-ELM 分类器，极限学习机应用，可以用于机器视觉应用、图像理解等各方面。-OS-ELM classifier, Extreme Learning Machine Can be used in all aspects of machine vision applications, such as image understanding.
OS-ELM(Python)
- ELM是一种简单易用、有效的单隐层前馈神经网络，该代码是用python实现的极限学习机，亲测有用-extreme learning machine realized by python，it works well
OS-ELM
- 极限学习机（extreme learning machine）ELM是一种简单易用、有效的单隐层前馈神经网络SLFNs学习算法。2004年由南洋理工大学黄广斌副教授提出。传统的神经网络学习算法（如BP算法）需要人为设置大量的网络训练参数，并且很容易产生局部最优解。极限学习机只需要设置网络的隐层节点个数，在算法执行过程中不需要调整网络的输入权值以及隐元的偏置，并且产生唯一的最优解，因此具有学习速度快且泛化性能好的优点。-ELM extre
OS-ELM
- 线性极限学习机，对于黄广斌提出的极限学习机进行改进后的代码-Limit linear learning machine, learning the limits proposed for Huangguang Bin native code will be improved
Enhancement-for-OS-ELM
- 在线序列极限学习机OS-ELM算法的基本原理及其改进算法- In this paper, we propose a Constructive Enhancement for OS-ELM (CEOS-ELM), which can add random hidden nodes one-by-one or group-by-group with fixed or varying group size.
Self--Adjustment-of-Neuron-I
- a self-adjustment algorithm based on GAP-RBF is proposed for solving how to choose the overlap factorfor calculating neuron impact width.-In this paper, we propose a Constructive Enhancement for OS-ELM (CEOS-ELM),
OS-ELM
- 是一种在线序列OS-ELM算法的MATLAB程序，可参考论文A Fast and Accurate Online Sequential Learning。(matlab codes for OS-ELM(Online Sequential ELM))
OS-ELM在线极限学习机
- 此代码是OS-ELM在线极限学习机，内含训练集和测试集。(This code is the OS-ELM online extreme learning machine, containing training set and test set.)
OS-ELM
- 在线ELM的MATLAB代码，可用于目标跟踪，回归分析预测、以及分类。(The MATLAB code of online ELM can be used for target tracking, regression analysis, prediction, and classification.)
OS-ELM (1)
- 可以实现对大量数据的实时在线预测及其分类效果(The real-time online prediction and classification effect of a large number of data can be realized)
ELM+JAVA
- OS-ELM识别分类代码，是一种很好的识别方法(os-elmNumberofHiddenNeurons - Number of hidden neurons assigned to the ELM % ActivationFunction - Type of activ)
OSELM
- OSELM主要代码以及测试代码，亲测有用(the code and test file of oselm)