similar to: Neural Networks

Hi everyone, I'm a graduate student of engineering, lately introduced with R. and using R for my project and thesis. I'm trying to use R for implementing a neural network regression model and apply it to my database. I found three R packages ("AMORE" , "grnnR" , "neural") in R website, but their manuals are not really user-friendly in my idea. I was wondering

hi all , has anyone tried to predict a univariate time series by a neural networks packages ? please help me in this problem . I am new in R and I did not found any document that explains this problem. thanks in advance David [[alternative HTML version deleted]]

Hi All, I am trying to learn Neural Networks. I found that R has packages which can help build Neural Nets - the popular one being AMORE package. Is there any book / resource available which guides us in this subject using the AMORE package? Any help will be much appreciated. Thanks, Indrajit

Hola a todos, en primer lugar quería agradecer la ayuda recibida desde el foro con respecto a la creación de una red neuronal. Estoy utilizando el paquete Neuralnet, que me parece que es bastante bueno, pero tengo el problema que soy incapaz de hacer las predicciones del modelo. Sé que se hace con el comando "compute", pero no encuentro ningún ejemplo de cómo hacerlo. ¿Alguien me puede

Hi, I'm currently trying desperately to get the nnet function for training a neural network (with one hidden layer) to perform a regression task. So I run it like the following: trainednet <- nnet(x=traindata, y=trainresponse, size = 30, linout = TRUE, maxit=1000) (where x is a matrix and y a numerical vector consisting of the target values for one variable) To see whether the network

Hi all, I am working for my master's thesis and I need to do a neural network to forecast stock market price, with also external inputs like technical indicators. I would like to know which function and package of R are more suitable for this study. Thanks a lot for your response, Arnaud TREBAOL. -- Arnaud Trébaol T.I.M.E. Student Ecole Centrale de Lille (09) Politecnico di Milano (10)

Hi! I am studying to use some R libraries which are applied for working with artificial neural neworks (amore, nnet). Can you recommend some useful, reliable and easy to get example data to use in R for creating and testing a neural network? __________________________________________________________________ Make your browsing faster, safer, and easier with the new Internet

Hi R, Here's a question/problem on the 'neuralnet' package for neural networks. I have more than 50 factors in each of my independent variables. When I apply the command 'neuralnet', I get the below warning message, > net.sum <- neuralnet( Sum~Var1+Var2+Var3, b, hidden=0,linear.output=TRUE) Warning message: 'predictions' will not be calculated, as at

Hi, I am having difficulty in training a neural network using the package "neuralnet". My neural network has 2 input neurons (covariates), 1 hidden layer with 2 hidden neurons and 2 output neurons (responses). I am training my neural network with a dataset that has been transformed so that each column is of type "numeric". The difficulty I am facing is that the responses of

Dear all, I have a variable that I would like also to use it as a string. The reasons is that I want to collect results from different function to one table.. So when I use the  colnames(mymatrix) <-c(function1.function2,function3) the function1, function2, function3 to be "converted" to simple strings so as  colnames(mymatrix)

Dear R users, I'd like to ask your guidance regarding the following two questions: (i) I just finished reading Chris Bishop's book "Neural Networks for Pattern Recognition". Although the book gave me good theoretical foundation about NN, I'm now looking for something more practical regarding architecture selection strategies. Is there any good reference about "best

PLEASE REMOVE ME FROM THIS LIST SERVE IMMEDIATELY!!!!!! On Wed, Dec 12, 2012 at 6:41 PM, dada <paxkn@nottingham.ac.uk> wrote: > Hi > I would like to do neural netowrk analysis on my data. It look like this: > > drug param1 param2 param3 param4 param5 class > A 111 15 125 40 0.5 1 > B 347 13 280 55 3 2 >

Sorry for these multiple postings. I solved the problem using na.omit() to drop records with missing values for the time being. I will worry about imputation, etc. later. I calculated the sum of squared errors for 3 models, linear regression, neural networks, and support vector machines. This is the first run. Without doing any parameter tuning on the SVM or playing around with the number of

Hi I would like to know how I can change the name of a model for each trainning cycle of a model. I work with the RSNNS package and to build a neural network, I used : for (i in 5:30) .... model_ANN <- mlp(X, Y, size=n,....) # where size is the number of neurons in the hidden layer but I need to save each time that the model that is build (the end of each cycle), e.g., when i = 5, I need to

I am exploring neural networks (adding non-linearities) to see if I can get more predictive power than a linear regression model I built. I am using the function nnet and following the example of Venables and Ripley, in Modern Applied Statistics with S, on pages 246 to 249. I have standardized variables (z-scores) such as assets, age and tenure. I have other variables that are binary (0 or 1). In

I am interested in Neural network models in R. Is there any reference material/tutorial which i can use. Regards, ____________________________________________________________________________________ TV dinner still cooling? Check out "Tonight's Picks" on Yahoo! TV.

As a follow-up to my email below: The input data frame to nnet() has dimensions: > dim(coreaff.trn.nn) [1] 5088 8 And the predictions from the neural network (35 records are dropped - see email below for more details) has dimensions: > pred <- predict(coreaff.nn1) > dim(pred) [1] 5053 1 So, the following line of R code does not work as the dimensions are

Hi, I am trying to build a VERY basic neural network as a practice before hopefully increasing my scope. To do so, I have been using package "neural" and the MLP related functions (mlp and mlptrain) within that package. So far, I have created a basic network, but I have been unable to change the default activation function. If someone has a suggestion, please advise. The goal of the

Hi all, I am trying to develop a neural network(Multilayer perceptron) with the package 'NEURALNET'. I have some doubts on it, 1. Whether this procedure taken care about categorical input variables- The reason is I could not find any option to describe type of variable in the arguments? 2.The algorithm is providing input variables generalized weights like this, >

Estimados Tengo un problema, R se cuelga (cierra, aborta, etc.) al usar códigos de neuralnet. Copio un ejemplo cualquiera para preguntar si a otros les pasa lo mismo o es un problema puntual en mi computadora (uso R revolution win 10). # install, load package install.packages(NeuralNetTools) library(NeuralNetTools) # create model library(neuralnet) AND <- c(rep(0, 7), 1) OR <- c(0, rep(1,