神经网络matlab代码

  • misterdududu
    了解作者
  • matlab
    开发工具
  • 176KB
    文件大小
  • rar
    文件格式
  • 0
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  • 1 积分
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  • 2022-04-12 03:59
    上传日期
新手必备,容易上手,可以看懂,神经网络学习帮手
SCN_release_v1.rar
  • SCN_release_v1
  • Demo_Classification.m
    1.7KB
  • Demo_Iris.mat
    2.9KB
  • ReadMe.txt
    2.2KB
  • SCN.m
    9.2KB
  • Demo_Regression.m
    1.8KB
  • Demo_Data1.mat
    71.7KB
  • Demo_Data.mat
    18.3KB
  • DEMO_DATA2.mat
    71.7KB
  • Tools.m
    6.2KB
  • dierzhang.m
    12.3KB
内容介绍
%***************************************************************************************** %************ This program is associated with our publication entitled ***************** %************ Stochastic Configuration Networks: Fundamentals and Algorithms ********* %************ Please cite this publication as a reference when you employ SCNs ********* %************ in your research, case studies, comparisons or real world applications ****** %***************************************************************************************** % SCN Code provided by DeepSCN (http://deepscn.com/publications.php) % Permission is granted for anyone to copy, use, modify, or distribute this % program and accompanying programs and documents for any purpose, provided % this copyright notice is retained and prominently displayed, along with % a note saying that the original programs are available from our % web page: http://deepscn.com/software.php. % The programs and documents are distributed without any warranty, express or % implied. As the programs were written for research purposes only, they have % not been tested to the degree that would be advisable in any important % application. All use of these programs is entirely at the user's own risk. How to make it work: 1. Create a separate directory and download SCN_release_v1.zip from http://deepscn.com/software.php. 2. Extract SCN_release_v1.zip to obtain the following 4 files: * SCN.m (Main file for building SCN models) * Tools.m (Supportive functions for SCN) * Demo_Regression.m * Demo_Classification.m Make sure the file names have not been changed. 3. For the toy experiments, run Demo_Regression.m and Demo_Classification.m in Matlab (to check that everything works fine, they have be tested on the 2016b and 2017a versions of Matlab). 4. For your own applications, make sure you have enough RAM and Disk to support the computation on your computing device. 5. The demos can be modified with new datasets. You can also change the parameters and the configurations for the SCN model in the code. Please contact us at (http://deepscn.com/people.php) if you find any bugs.
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