4 revert
net = revert ( net ) 0 3.19
adapt sim train init
5 train
[net, TR, Y , E, Pf, Af] = train ( net, P, T, Pi, Ai, VV , TV )
train ainFcn ainParam net P T Pi Ai VV TV TR Y E Pf Af P T Pi Ai VV TV Y E Pf Af
VV TV VV 3.19
adapt sim init revert
6 sim
maxstep
[Y , Pf, Af, E, perf ] = sim (net, P , Pi, Ai, T)
[Y , Pf, Af, E, perf ] = sim (net, {Q TS}, Pi, Ai, T)
[Y , Pf, Af, E, perf ] = sim (net, Q, Pi, Ai, T)
sim sim net P Pi Ai T Y Pf Af E perf P T Pi Ai Y E Pf Af sim Q TS
3.19 P = {0 -1 1 1 0 -1 1 0 0 1 10 -1 -1 1 1 1 0 -1}; T = {0 -1 0 2 1 -1 0 1 0 1 2 1 -1 -2 0 2 2 1 0}; P T P T net =newlin ( [-1 1], 1, [0 1], 0.005 ); [-1 1] 0.005 3.14
net = initlay ( net )
info = initlay ( code )
net.layers{i}.initFcn net code pnames pdefaults initlay
newp newlin newff newcf initlay initlay
(1) net.initFcn initlay initlay
[ ]
(2) net.layers{i}.initFcn
initwb initnw init
1 initnw Nguyen-Widrow
net = initnw ( net, i )
Nguyen-Widrow net i i dotprod netsum initnw
newff newcf initnw i initnw (1) net.initFcn initlay (2) net.layers{i}.initFcn initnw
initwb initlay init
2 initwb net = initwb ( net, i ) net i
newp newlin initnw i initwb (1) net.initFcn initlay (2) net.layers{i}.initFcn initwb (3) net.inputWeights{i,j}.initFcn net.layerWeights{i,j}.initFcn net.biases{i}.initFcn
initnw initlay init
3..2.5
3-6
i
(1) net.initFcn initlay (2) net.layers{i}.initFcn initwb (3) net.inputWeights{i,j}.initFcn net.layerWeights{i,j}.initFcn net.biases{i}.initFcn
1 initcon B = initcon ( S, PR ) learncon S PR [Pmin Pmax] [1 1] B 3.20 initcon b = initcon (
2 ) b = 5.4366 5.4366 initwb initlay init learncon
2 initzero W = initzero ( S, PR ) B = initzero ( S, [1 1] ) S PR [Pmin Pmax] W B
initwb initlay init
3 midpoint W = midpoint ( S PR ) S PR [Pmin Pmax] W (Pmin+Pmax)/2
4 randnc W = randnc ( S, PR ) W = randnc ( S, R ) S PR [PminPmax] R W
5 randnr W = randnr ( S, PR ) W = randnr ( S, R ) S PR [Pmin Pmax] R W
6 Rands 0. W = rands ( S, PR ) M = rands ( S, R ) B = rands ( S ) S PR [Pmin Pmax] R
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