Entrenamiento de neuronas usando matlab

“Matlab y redes neuronales”

>> net = newff([0 1; 0 1], [2 1], {'logsig', 'logsig'})

net =
Neural Network object:

architecture:

numInputs: 1
numLayers: 2biasConnect: [1; 1]
inputConnect: [1; 0]
layerConnect: [0 0; 1 0]
outputConnect: [0 1]

numOutputs: 1 (read-only)
numInputDelays: 0 (read-only)numLayerDelays: 0 (read-only)

subobject structures:

inputs: {1x1 cell} of inputs
layers: {2x1 cell} of layers
outputs: {1x2 cell} containing 1 outputbiases: {2x1 cell} containing 2 biases
inputWeights: {2x1 cell} containing 1 input weight
layerWeights: {2x2 cell} containing 1 layer weight

functions:

adaptFcn:'trains'
divideFcn: (none)
gradientFcn: 'gdefaults'
initFcn: 'initlay'
performFcn: 'mse'
plotFcns: {'plotperform','plottrainstate','plotregression'}trainFcn: 'trainlm'

parameters:

adaptParam: .passes
divideParam: (none)
gradientParam: (none)
initParam: (none)
performParam: (none)trainParam: .show, .showWindow, .showCommandLine, .epochs,
.time, .goal, .max_fail, .mem_reduc,
.min_grad, .mu, .mu_dec, .mu_inc,.mu_max

weight and bias values:

IW: {2x1 cell} containing 1 input weight matrix
LW: {2x2 cell} containing 1 layer weight matrix
b: {2x1 cell}containing 2 bias vectors

other:

name: ''
userdata: (user information)

>> input = [1 1 0 0; 1 0 1 0]

input =
1 1 0 0
1 0 10

>> output=sim(net, input)

output =
Columns 1 through 3

0.0438 0.5581 0.0964

Column 4

0.6759

>> target = [0 1 1 0]

target =
0 1 1 0

>>...