Greetings list, Is there a way of creating a simple feedforward neural network with AMORE? I know this is possible using the "neural" package, but that package is rather slow compared to AMORE. It seems that AMORE needs a vector that contains the number of neurons in the input, output *and* hidden layer. I've tried placing 0 as the number of neurons in the hidden layer but that resulted in bringing down R :(. Perhaps I'm doing something wrong? Wee-Jin p.s. Simple sample code that should demonstrate the problem. --------------------------------------------- library(AMORE) inputs <- matrix(0, 4, 2) inputs[,1] <- c(-1, -1, 1, 1) inputs[,2] <- c(-1, 1, -1, 1) target <- matrix(0, 4, 1) target[,1] <- c(-1, 1, 1, -1) #replace n.neurons=c(2,3,1) if we want a network with 3 hidden units. net <- newff(n.neurons=c(2,0,1), learning.rate.global=0.1, momentum.global=0.5, error.criterium="LMS", Stao=NA, hidden.layer="tansig", output.layer="tansig", method="ADAPTgdwm") result <- train(net, inputs, target, error.criterium="LMS", report=TRUE, show.step=50, n.shows=5) y <- sim(result$net, inputs)