Description: A matlab code for a reinforment learning routing problem-A Matlab code for a reinforment learning ro uting problem Platform: |
Size: 1024 |
Author:william |
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Description: 人工神经网络就是模拟人思维的第二种方式。这是一个非线性动力学系统，其特色在于信息的分布式存储和并行协同处理。虽然单个神经元的结构极其简单，功能有限，但大量神经元构成的网络系统所能实现的行为却是极其丰富多彩的。
-Simulation of artificial neural network is a second way of human thinking. This is a nonlinear dynamic system, which features a distributed information storage and parallel co-processing. Although the structure of single neurons is extremely simple, limited functionality, but a large number of neurons in a network system can realize the behavior is very colorful. Platform: |
Size: 5120 |
Author:prince |
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Description: 本文 通 过 对己有模型和锅炉运行现状的分析，尝试用BP神经网络的方法分析讨论锅炉效率在线计算和运行优化等问题。研究工作主要包括:基于BP神经网络进行煤的工业分析结果和元素分析结果之间的转换 考虑到煤在锅炉中燃烧时有固体未燃碳存在，给出了煤组成成分的实际结果的概念 详细讨论了过量空气系数的两种定义及特点，说明运行中用烟气分析结果确定过量空气系数时，对测得的湿烟气含氧量进行修正的必要性和方法:以反平衡法为基础给出了改进的锅炉效率在线计算模型 以效率在线计算为基础，尝试运用神经网络方法确定锅炉运行中的最佳过量空气系数。-Ac co rdi ngt ot hea nalysiso fa vailablem odelsa ndb oilerru nning
conditions, the problems in relation to the on-line calculation for
boiler efficiency and optimization operation can be discussed with
BPn euraln etworki nt histh esis.T her esearchw orkm ainlyc onclude:
The transformation between the industrial analysis results and the
element analysis results of coals based on neural network is discussed.
The concept of the actual results of coal ingredients is put forward
because there are some solid carbon not burned when coals burn in
boiler furnace. The two definitions of the excess air coefficient is
discusses in details. The necessity and methods of modifying the
oxygen content in damp gas are explained when the excess air
coefficient are determined with the results of gas analysis. The
modified on-line calculation model for the boiler efficiency on the
basis of the negative equation way is developed. Neural network
methods to determine the optimum excess air coef Platform: |
Size: 77824 |
Author:刘伟 |
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Description: 提取灰度共生矩阵的分类作用，有利于后续的人工神经网络的进行-To extract the classification of the gray level co-occurrence matrix, is advantageous to the subsequent artificial neural network Platform: |
Size: 4096 |
Author:段宏伟 |
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Description: BCI_MI_CSP_DNN是一种基于matlab的运动图像脑电信号分类程序。
基于matlab深度学习工具箱编写了BCI_MI_CSP_DNN程序
本程序的原理基于CSP和DNN算法
这个程序的性能是基于BCI竞赛II数据集II
提出了一种基于深度学习的运动图像脑电信号分类方法。在预处理原始脑电图信号的基础上，采用共空间模型(CSP)方法提取脑电图特征矩阵，并将其输入深度神经网络(DNN)进行训练和分类。我们的工作在BCI Competition II Dataset III上进行了实验测试，提出了最佳的DNN框架，准确率达到83.6%。(In this study, our goal was to use deep learning methods to improve the classification performance of motor imagery EEG signals. Therefore, we propose a classification method based on deep learning for motor imagery EEG signals. Based on the pre-processed raw EEG signals, a co-space model (CSP) method is used to extract the EEG feature matrix, which is then fed to a deep neural network (DNN) for training and classification. Our work was tested experimentally on the BCI Competition II Dataset III dataset, and the best DNN framework was proposed, achieving an accuracy of 83.6%.) Platform: |
Size: 14833664 |
Author:渔舟唱晚1 |
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