MATLAB: RBF NN using newrb for prediction one target “output vector” from many inputs factors ” 15 vectors”

Deep Learning Toolboxneural networkradial basis neural networks

 I am working in a research for forecasting electricity price. I am using a RBF network "newrb" for predicting electricity price . the network has 15 inputs (different factors) and one output(electricity price). i am asking how to do that using newrb for different Input & Output matrices ( 15 input predictors for one Output).

net = newrb(P,T,goal,spread,MN,DF).

P: R-by-Q matrix of Q input vectors.

T: S-by-Q matrix of Q target class vectors.

in my case P matrix ≠ T matrix

Best Answer

Search the NEWSGROUP and ANSWERS using

 greg newrb

Concentrate on the ones used for classification and pattern-recognition.

Hope this helps.

Thank you for formally accepting my answer

Greg

Related Solutions

MATLAB: Radial basis neural network

 help newrb
 doc newrb
 type newrb
 Separately search the NEWSGROUP and ANSWERS with
 greg newrb  ( 148 NEWSGROUP hits , 54 ANSWERS hits)

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: How to interpolate for ” not strictly monotonic increasing vectors”

Use griddedinterpolant, if your known points are properly gridded, or scatteredinterpolant if not.

If your known points are properly gridded, you first need to convert them into a grid:

Speed = [22 22 22.5 22.5]; 
Angle = [130 180 130 180]; 
RPM = [91.1490 91.2120 93.2234 93.2860];
Speedgrid = reshape(Speed, [2 2]);
Anglegrid = reshape(Angle, [2 2]);
RPMgrid = reshape(RPM, [2 2]);
g = griddedInterpolant(Anglegrid, Speedgrid, RPMgrid); %Angle and speed need to be in this order, or the whole lot transposed
%query for speed = 25, angle = 150:
rpm25_150 = g([150, 25])

Using scattered interpolant:

s = scatteredInterpolant(Speed', Angle', RPM')
rpm25_150 = s([25, 150])

Best Answer

Related Solutions

MATLAB: Radial basis neural network

MATLAB: How to interpolate for ” not strictly monotonic increasing vectors”

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