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[AI-NN-PR] Hermit多项式在线学习ran算法 DL : 0: 本程序用资源分配网(Resource_Allocation Network,简称RAN)实现了Hermit多项式在线学习问题。训练样本产生方式如下，样本数400，每个样本输入Xi在区间[-4,4]内随机产生（均匀分布），相关样本输出为F(Xi) = 1.1(1-Xi + Xi2)exp(-Xi2/2),测试样本输入在[-4，+4]内以0.04为间隔等距产生，共201个样本。训练结束后的隐节点为：11个，训练结束后的平均误差可达：0.03 -this program resources distribution network (Resource_Allocation Network , RAN) to achieve the Hermit polynomial online learning problems. Training samples have the following manner, the number of 400 samples, each sample interval in the importation of Xi [-4, 4] within randomly generated (evenly distributed) sample output related to F (Xi) = 1.1 (1-Xi Xi2) exp (-Xi2/2), test samples of the imported [-4, 4] to within 0.04 of equidistant spacing have a total of 201 samples. After the training of hidden nodes : 11, after the end of the training error of up to the average : 0.03
Date : 2025-12-26 Size : 8kb User : 刘波
[AI-NN-PR] XCSR_DE1.0 DL : 0: - XCS for Dynamic Environments + Continuous versions of XCS + Test problem: real multiplexer + Experiments: XCS is explored in dynamic environments with different magnitudes of change to the underlying concepts. +Reference papers: H.H. Dam, H.A. Abbass, C.J. Lokan, Evolutionary Online Data Mining – an Investigation in a Dynamic Environment. 2005, accepted for a book chapter in Springer Series on Studies in Computational Intelligence H.H. Dam, H.A. Abbass, C.J. Lokan, Be Real! XCS with Continuous-Valued Inputs. IWLCS 2005, (International Workshop on Learning Classifier Systems). Washington DC, June 2005.-- XCS for Continuous Dynamic Environments Test versions of XCS problem : real multiplexer Experiments : XCS is explored in dynamic environments with di fferent magnitudes of change to the underlying concepts. Reference papers : H. H. Dam, H. A. Abbass, C. J. Lokan. Evolutionary Online Data Mining-an Investiga tion in a Dynamic Environment. 2005. accepted for a book chapter in Springer Series o n Studies in Computational Intelligence H.H. D am, H. A. Abbass, C. J. Lokan. Be Real! XCS with Continuous- Valued Inputs. IW LCS 2005. (International Workshop on Learning Classifi er Systems). Washington DC, June 2005.
Date : 2025-12-26 Size : 22kb User : 李恆寬
[AI-NN-PR] tspGeneticAlgorithm DL : 0: 一个遗传算法求解TSP问题的具体实现，采用C++实现，可以采用网上提供的城市节点数据测试-A genetic algorithm for TSP on the specific implementation, using C++ implementation, available online the city can use the node data test
Date : 2025-12-26 Size : 146kb User : wangrenbiao
[AI-NN-PR] identification-of-linear-system DL : 0: 线性网络在线性系统辩识中的应用。 1 网络设计 2 网络性能检验-Linear Network Online Application System Identification. 1 Network Design 2 Network performance test
Date : 2025-12-26 Size : 28kb User : m
[AI-NN-PR] OS-ELM在线极限学习机 DL : 1: 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.)
Date : 2025-12-26 Size : 131kb User : 条子
[AI-NN-PR] utf8''Traffic-sign-recognition DL : 0: 项目基于Tensorflow进行实现。 #### 文件说明： --- * input_data.py: 图片的输入 * traffic_sign_cnn.py: 用cnn进行训练分类 * testDemo.py: 用于测试已经训练出来的模型，输入单个图片输出结果，并分类到文件夹 #### 数据集说明： --- * 这里是列表文本使用的是比利时的交通标志数据集，可以网上自己找，里面有62个分类。 #### 网络说明： --- * 这里是列表文本这里是列表文本CNN网络包含两个卷积层，两个全连接层。识别率大概在95% 左右，可以自己根据需要自己修改参数提高识别率另外，训练开始前需要先在项目目录下新建文件夹./log/train/,用来保存模型参数,数据集的目录结构大概是./data/train/00001（标签）/图片(The project is based on Tensorflow. #### File Description: --- * input_data.py: input of the picture * traffic_sign_cnn.py: Training classification with cnn * testDemo.py: Used to test the trained model, enter the result of a single image output, and categorize it into a folder #### Data Set Description: --- * Here is a list of texts using the Belgian traffic sign data set, which can be found online, with 62 categories. #### Network Description: --- * Here is the list text Here is the list text The CNN network consists of two convolutional layers, two fully connected layers. The recognition rate is about 95%, you can modify the parameters by yourself to improve the recognition rate In addition, before the start of training, you need to create a new folder in the project directory. / Log / train /, used to save the model parameters, the directory structure of the data set is probably ./data/train/00001 (tag) / picture)
Date : 2025-12-26 Size : 420kb User : lionkiss