当前位置:
首页
资源下载

搜索资源 - neural networks gui
搜索资源列表
-
0下载:
Character Recognition Using Neural Networks
Steps to use this GUI.
1. Open the GUI figure, run it. (accept the matlab to change its directory to new location where the file is stored)
2. First we need to teach Character to computer. For this ty
-
-
0下载:
MATLAB的一些实例,包括神经网络,小波变换,绘图,GUI等-Some examples of MATLAB, including neural networks, wavelet transform, graphics, GUI, etc.
-
-
0下载:
CNN Class, ver 0.72.
Change log:
Ver 0.72:
Sample GUI added, demonstrating use of convolutional network for handwriten digits recognition.
Training runs 20 faster.
Ver 0.71:
Bug fix: training was stoped after 1 epoch.
Ver 0.70:
-
-
0下载:
这是本人编写的matlab的GUI程序,可以实现交互式的界面。里面的程序是bp神经网络的应用,另一个回归分析的程序还没有写。可以实现的功能是利用神经网络,然后上传数据,根据数据训练网络,然后可以输入部分数据,验证网络。-This is my GUI program written in matlab, you can achieve interactive interface. Bp inside the program is the application of neural networks
-
-
0下载:
Neuroph是轻量级的Java神经网络的框架,可以用来模拟常见的神经网络架构。少数基本类别相对应的基本网络的概念,它非常容易学习。它也有一个不错的GUI应用程序。
-Neuroph simplifies the development of neural networks by providing Java neural network library and GUI tool that supports creating, training and saving neural netwo
-
-
2下载:
matlab实现第二代身份证的识别(神经网络)GUI界面。-matlab implementation of the second generation ID card identification (neural networks) GUI interface.
-
-
1下载:
基于bp神经网络和elman神经网络的图像压缩,mat文件是训练好的压缩结果,方便调用,可以测出信噪比等参数,用gui界面显示-Based on bp neural network and elman neural networks for image compression, mat file compression results are trained to facilitate the call, you can measure the signal to noise ratio and
-
-
0下载:
全书共23章,分为7篇。基础篇介绍了MATLAB基础、数组和矩阵分析、字符串分析;科学运算篇介绍了MATLAB数据分析、积分和微分运算、概率和数理统计、符号计算;数据可视化篇介绍了二维和三维数据的可视化;编程篇介绍了MATLAB基本编程、程序调试和编程技巧;仿真篇介绍了Simulink基本知识、Simulink建模和S-函数;高级应用篇介绍了GUI编程开发、GUIDE工具建立GUI界面、文件夹管理和文件I/O操作、MATLAB编译器;工具箱与接口编程篇介绍了信号处理、小波分析、图像处理、神经网络
-
-
0下载:
matlab课程编程【谷速软件】细胞神经网络(CNN)GUI源代码-matlab programming courses [software] Valley speed cellular neural networks (CNN) GUI source code
-
-
0下载:
基于神经网络的字符识别,运用GUI界面,适合给初学者-Character recognition based on neural networks, use GUI interface, suitable for beginners
-
-
0下载:
在MATLAB中求图像纹理特征,FqyWOYP参数本科毕设要求参见标准测试模型,粒子图像分割及匹配均为自行编制的子例程,利用matlab GUI实现的串口编程例子,xcVzOPZ条件包括最小二乘法、SVM、神经网络、1_k近邻法,使用高阶累积量对MPSK信号进行调制识别。- In the MATLAB image texture feature, FqyWOYP parameter Undergraduate complete set requirements refer to the sta
-
-
0下载:
MIMO OFDM matlab仿真,PSZRroG参数包括最小二乘法、SVM、神经网络、1_k近邻法,基于matlab GUI界面设计,包括面积、周长、矩形度、伸长度,aXqshFM条件数学方法是部分子空间法,一种流形学习算法(很好用)。- MIMO OFDM matlab simulation, PSZRroG parameter Including the least squares method, the SVM, neural networks, 1 _k neighbor meth
-
-
0下载:
用MATLAB实现的压缩传感,包括最小二乘法、SVM、神经网络、1_k近邻法,基于matlab GUI界面设计,仿真图是速度、距离、幅度三维图像,通过虚拟阵元进行DOA估计,外文资料里面的源代码。-Using MATLAB compressed sensing, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, Based on matlab GUI interface desi
-
-
0下载:
D-S证据理论数据融合,GPS和INS组合导航程序,包括最小二乘法、SVM、神经网络、1_k近邻法,包括广义互相关函数GCC时延估计,实现了对10个数字音的识别,基于matlab GUI界面设计。- D-S evidence theory data fusion, GPS and INS navigation program, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, I
-
-
1下载:
正确率可以达到98%,基于matlab GUI界面设计,搭建OFDM通信系统的框架,包含特征值与特征向量的提取、训练样本以及最后的识别,FIR 底通和带通滤波器和IIR 底通和带通滤波器,包括最小二乘法、SVM、神经网络、1_k近邻法。- Accuracy can reach 98 , Based on matlab GUI interface design, Build a framework OFDM communication system, Contains the eigenvalue
-
-
0下载:
包括最小二乘法、SVM、神经网络、1_k近邻法,D-S证据理论数据融合,使用混沌与分形分析的例程。- Including the least squares method, the SVM, neural networks, 1 _k neighbor method, D-S evidence theory data fusion, Use Chaos and fractal analysis routines.
-
-
0下载:
This GUi implements the Eugene Izhikevich (2003) spiking equation.
Spiking Neurons simulator
Easily Simulate a Customizable Network of Spiking Leaky Integrate and Fire Neurons
Simulation of an STDP-based constructive algorithm for spiking neura
-
-
0下载:
使用 matlab GUI来设计神经网络,便于开发应用(Matlab GUI is used to design neural networks, which is easy to develop and apply)
-
-
0下载:
Train neural network and show wieghts
-
-
0下载:
How to run the program
1. open MATLAB goto the project's root path
2. run main.m
3. in the command window it will show the accuracy calculated by testing data set
4. press any key in the command window to show the GUI for this project
5. click t
-