path: root/help/NLMSCC~-help.pd

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#X text 35 38 internal downsampling of update;
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#X text 76 364 input signal;
#X text 191 363 desired signal;
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#X text 30 409 y(n) = W * x(n);
#X obj 277 292 FIR~ IR 32;
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#X text 473 280 (array-sizes have to be >= then FIR_size);
#X text 423 270 1.arg: <float> number of order of FIR-filter;
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#X text 607 70 load;
#X text 652 79 IR;
#X text 5 396 filtered output signal;
#X text 169 396 error signal;
#X text 143 408 e(n) = d(n) - W * x(n);
#X text 294 458 constrain;
#X text 294 468 coefficients;
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#X text 614 162 update of W;
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#X text 423 310 4.arg: <symbol> table-name of W;
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#X text 423 330 6.arg: <symbol> table-name of upper boundary of W;
#X text 423 300 3.arg: <float> minimum input value gamma;
#X text 133 318 minimum input value;
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#X text 281 131 beta;
#X text 232 148 >;
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#X text 278 143 2;
#X text 341 143 2;
#X text 312 148 gamma * blocksize);
#X text 295 148 +;
#X text 244 148 x[n-i];
#X text 175 137 my(n) =;
#X text 224 132 _________________________________;
#X text 29 138 normalized learn rate:;
#X text 246 243 <;
#X text 250 237 |;
#X text 250 249 |;
#X text 250 230 |;
#X text 250 256 |;
#X text 250 270 |;
#X text 250 223 |;
#X text 254 206 _;
#X text 253 270 _;
#X text 271 211 W > W_top;
#X text 265 275 W < W_bottom;
#X text 260 246 &&(W <= W_top);
#X text 256 235 (W >= W_bottom);
#X text 175 243 W(k+1 \, i) =;
#X text 250 216 |;
#X text 250 263 |;
#X text 346 275 ...... W(k+1 \, i) = W_botton(i);
#X text 334 212 ........ W(k+1 \, i) = W_top(i);
#X text 174 56 y(n) = W * x(n);
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#X text 174 74 e(n) = d(n) - W * x(n);
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#X text 120 75 error:;
#X text 117 57 output:;
#X text 27 242 coefficient iteration:;
#X text 358 243 .... W(k+1 \, i) = W(k \, i) + my(n)* e(n)* x(n);
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#X text 426 221 Normalized Least Mean Square (linear adaptive FIR-filter)
;
#X text 455 232 with Coefficient Constraint;
#X text 422 350 (C) 2005 \, m.noisternig & t.musil \, IEM \, Graz \,
Austria;
#X text 450 363 [noisternig \, musil]_AT_iem.at;
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