path: root/adaptive/doc/help-nlms2~.pd

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#X text 89 219 input signal x[n];
#X text 182 252 reference signal d[n];
#X text 182 267 (desired signal);
#X text 108 461 output signal y[n];
#X text 35 166 init arg1: nr. of coefficients;
#X text 35 179 init arg2: stepsize parameter mu;
#X text 221 646 (c) Georg Holzmann <grh@mur.at> \, 2005;
#X text 39 520 some more info:;
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#X text 350 38 adaptive systems;
#X text 360 54 for Pure Data;
#X text 35 599 in the example folder !;
#X text 35 586 For much more examples see patches;
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#X text 93 134 outputs for e[n] and c[n];
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#X text 153 413 error signal e[n];
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#X text 169 382 c0[n];
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#X text 135 322 coefficients:;
#X text 36 122 nlms2~: same as nlms~ \, but with additional;
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#X text 36 87 x[n];
#X text 131 89 d[n];
#X text 31 234 y[n];
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#X text 115 28 x[n] = 2 \, d[n] = 1 \, N = 1 (= nr. of coefficients)
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#X text 26 29 EXAMPLE:;
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#X msg 341 220 adaptation 1;
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#X obj 198 207 s \$0-lms;
#X msg 198 171 mu \$1;
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#X text 275 147 <- try different mu;
#X msg 199 109 clear;
#X text 242 110 <- clear to start new adaptation;
#X text 189 461 -- 1024 samples --;
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#X text 195 693 -- 1024 samples --;
#X text 47 510 squared error e^2[n] (learning curve):;
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#X restore 38 540 pd NLMS2_EXAMPLE;
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#X text 35 135 x[n] ... input signal of the system;
#X text 35 120 c[n] ... coefficient vector of the system;
#X text 35 104 y[n] ... output signal of the system;
#X text 35 398 d[n] ... desired signal \, reference signal;
#X text 50 74 -> y[n] = c0[n]*x[n] + c1[n]*x[n-1] + c2[n]*x[n-2] +
...;
#X text 35 312 mu ... step-size parameter (learning rate);
#X text 34 282 c[n] ... new coefficient vector;
#X text 34 297 c[n-1] ... old coefficient vector;
#X text 34 354 e[n] ... error sample at time n \, LMS tries to minimize
this error;
#X text 35 382 x[n] ... tap-input vector at time n;
#X text 71 241 with e[n] = d[n] - y[n];
#X text 33 33 An adaptive system is simply a FIR filter with the coefficients
c[n] \, which can be learned.;
#X text 36 440 How to choose mu ?;
#X text 36 463 Sufficient (deterministic) stability condition:;
#X text 32 195 The normalized LMS Adaptation Algorithm:;
#X text 70 226 c[n] = c[n-1] + mu/(alpha+abs(x[n])^2) *e[n]*x[n];
#X text 34 327 alpha ... a small positive constant \, only to avoid
division by zero;
#X text 152 490 0 < mu < 2;
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#X msg 387 352 getmu;
#X msg 387 331 mu \$1;
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#X msg 387 460 getN;
#X msg 387 549 help;
#X msg 387 209 clear;
#X msg 387 276 print;
#X msg 387 517 read demo.dat;
#X msg 387 173 getadaptation;
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#X msg 387 151 adaptation \$1;
#X msg 387 496 write demo.dat;
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#X text 490 151 turn adaptation on/off;
#X text 435 203 clear current coefficients;
#X text 435 216 and set them back to 0;
#X text 436 275 print current coefficients;
#X text 438 335 set/get stepsize parameter;
#X text 439 349 mu (learning rate);
#X text 428 460 get Nr. of coefficients;
#X text 498 513 and mu to file;
#X text 498 499 write/read coefficients;
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#X msg 387 401 alpha \$1;
#X msg 387 422 getalpha;
#X text 456 403 set/get alpha (normally;
#X text 457 417 you don't need that);
#X msg 387 246 init_unity;
#X text 467 233 set first coefficient to 1 \,;
#X text 469 246 all others to 0 (= delay;
#X text 468 259 free transmission);
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