Rank: 38462 based on 0 downloads (last 30 days) and 0 files submitted

Chan-Yun

E-mail: cy.yang@tsint.edu.tw

Personal Profile:
Professional Interests:

Watch this Author's files

Files
Watch Lists

Comments and Ratings by Chan-Yun		1 - 1 of 1
Updated	File	Comments	Rating
28 Jul 2009	ANN Adaptive Neural Networks Author: Giampiero Campa	The blocksets are considerably beneficial to my application. Can I ask further for a more detailed descriptions or references of the function block "GRBFNN"? There are many parameters alone with the block. As you know, with improper parameter settings, the block may be used unsatisfactory and lead to an incorrect result.

Currently Watching
Type	Description
File	ANN

Files Matching Chan-Yun's Watch List					1 - 1 of 1
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
14 Feb 2008		ANN Adaptive Neural Networks Author: Giampiero Campa	neural networks, adaptive neural netwo..., ebpa, adaline, fuzzy logic, rbf	105	41	4.0 4.0 \| 46 ratings

Comments and Ratings Matching Chan-Yun's Watch List			1-5 of 64
Date	File	Comment by	Comments	Rating
13 Dec 2011	ANN Adaptive Neural Networks	Giampiero Campa	For 64bit machines you just have to recompile the code. Just delete the mex32 files, rename src.z.p to src.zip, extract the c code, and use mex to generate 64 bits exetutables.	Comment only
20 Oct 2011	ANN Adaptive Neural Networks	Ricky	Are there any plans to make this library 64-bit compatible? I am trying to update some code to the current version of Matlab/Simulink in a classroom/lab environment where the 64-bit version is installed	Comment only
09 Oct 2011	ANN Adaptive Neural Networks	Mr Smart		5
28 Jul 2011	ANN Adaptive Neural Networks	saurabh mishra	Thanks Victor for the help.. I tried as per your suggestions but it's not working...:( I had seen that linearly separable inputs doesn't need hidden layers.But when I tried , it's not working completely.	Comment only
28 Jul 2011	ANN Adaptive Neural Networks	Victor Gomez	I think your code is OK but remember this... Neural networks doesn't have a procedure or metodology for an aplication, you need to try differents things according to your objetive, i'll give you some basic suggestions: 1. - Try with another NN structure, choose another arquitecture, for example 2 layers with 5 neurons each one: net = newff(pt,t,[5,5]); 2.- Get down your performance goal (remember it's a cuadratic error) net.trainParam.goal = 0.000001; % or more 3.- Use neural networks GUI in MATLAB, this is useful for better understanding. you can modify easily the structure and some parameters. Write "nntool" in the command window, 4.- To use and visualize your net in simulink as blocks, use the command "gensim(net,-1)" (net is the net's name) this is useful for analize each node of your net for example i did this: pt = random(10,7) % pt=input t = [4 9 16 25 36 49 64]; % t = target net = newff(pt,t,[5 5]); net.trainParam.goal = 0.01; net = train(net,pt,t); y = sim(net,pt); y = 19.8006 8.9843 16.0019 25.0058 16.8919 49.0029 63.9999 t = [4 9 16 25 36 49 64]; % t = target OK it wasn't the best result haha.. but you need to modify some things and try and try until you are satisfied. I hope this were helpful for you.. Regards!	Comment only

Contact us at files@mathworks.com