ann.mdl

The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural

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  Created		  "Mon May 01 18:03:26 2000"
  Creator		  "CAMPA"
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  LastModifiedDate	  "Fri Jun 27 18:28:49 2003"
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      MaskDescription	      "  Self Adaptive Discrete Time Linear Neural Net"
"work\n"
      MaskHelp		      "<p>\n  This Neural Network is used to adaptivel"
"y approximate\n  an affine scalar field y=W*x+k, with the input vector \n  x "
"being a function of time.\n</p>\n<p>\n  The first input is x. <br>\n  The sec"
"ond input is the error signal (i.e. e=y-ys). <br>\n  The third input is the l"
"earning enable:\n       with LE=1 the learning is enabled, \n       with LE=0"
" the learning is disabled.\n</p>\n<p>\n  The first output is the learned func"
"tion ys(x). <br>\n  The second output is the states vector. <br>\n  Since thi"
"s Adaline implementation is single output,\n  i.e. ys(x) is a scalar, the wei"
"ght matrix W is actually\n  a row vector of size Ni+1 where Ni is the dimensi"
"on \n  (the number of elements) of x.\n</p>\n<p>\n  The first parameter in th"
"e mask is the learning rate.\n</p>\n<p>\n  The second parameter in the mask i"
"s the stabilizing factor.\n</p>\n<p>\n  The initial condition could be either"
" a vector of Ni+1 \n  parameters or a scalar. In the latter case, the scalar"
"\n  is repeated Ni+1 times to form the initial condition vector.\n</p>\n<p>\n"
"  OUTPUT EQUATION: <br>\n  At any given time t, if x(t) is the input vector, "
"\n  then the output of the neural network is : <br>\n  ys=0.1*Wo+Ws*x(t) <br>"
"\n  where Wo and Ws are respectively the first \n  element and the rest of th"
"e state vector. <br>\n<br>\n  STATE EQUATION (Learning Algorithm): <br>\n  Th"
"e state vector W(t) is updated according to the gradient rule:\n  W(t+T)=W(t)"
"-eta*(dys/dW)*e(t)-eta*gamma*W(t) <br>\n  where eta is the learning rate, dys"
"/dW is a jacobian matrix,\n  gamma is the stabilizing factor (sigma modificat"
"ion),\n  e(t)=y(t)-ys(t) is the error vector at time t, and T is the sampling"
" time.\n</p>\n<p>\n  The final mask parameter is the sampling time of the blo"
"ck, T.\n</p>\n<p>\n  For further reference see some papers on the ADALINE NN."
"<br>\n  This block is implemented in Simulink.\n</p>\n<p>\n  Giampiero Campa,"
" June 20 2003\n</p>"
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