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-rw-r--r--help/NLMSerr_in~-help.pd284
1 files changed, 136 insertions, 148 deletions
diff --git a/help/NLMSerr_in~-help.pd b/help/NLMSerr_in~-help.pd
index 6882aea..da3eb5d 100644
--- a/help/NLMSerr_in~-help.pd
+++ b/help/NLMSerr_in~-help.pd
@@ -1,78 +1,35 @@
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-#X text 516 230 2.arg: <float> learn-rate = beta;
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-#X text 34 37 internal downsampling of update;
-#X msg 96 337 gamma \$1;
-#X text 75 363 input signal;
-#X text 190 362 desired signal;
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-#X obj 276 291 FIR~ IR 32;
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-#X text 566 220 (array-sizes have to be >= then FIR_size);
-#X text 516 210 1.arg: <float> number of order of FIR-filter;
-#X text 42 71 update every sample;
-#X text 42 56 stop \, no update;
-#X text 42 86 update every 2nd sample;
-#X text 42 101 update every 4th sample;
-#X text 42 116 update every 8th sample;
-#X text 42 131 update every 16th sample;
-#X text 42 146 update every 32nd sample;
-#X text 42 161 update every 64th sample;
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+#X text 712 267 (array-sizes have to be >= then FIR_size);
+#X text 662 256 1.arg: <float> number of order of FIR-filter;
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+#X text 630 38 load;
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#X text 4 495 filtered output signal;
-#X text 178 495 error signal;
-#X text 152 507 e(n) = d(n) - W * x(n);
-#X text 293 557 constrain;
-#X text 293 567 coefficients;
-#X msg 276 522 0;
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+#X text 395 422 error signal;
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#X text 116 55 output:;
#X text 26 210 coefficient iteration:;
#X text 188 211 W(k+1 \, i) = W(k \, i) + my(n)* e(n)* x(n);
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-#X text 518 169 Normalized Least Mean Square (linear adaptive FIR-filter)
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-#X text 514 268 (C) 2005 \, m.noisternig & t.musil \, IEM \, Graz \,
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-#X text 542 281 [noisternig \, musil]_AT_iem.at;
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+#X text 660 364 (C) 2010 \, t.musil & m.guldenschuh \, IEM \, Graz
+\, Austria;
+#X text 688 377 [musil | guldenschuh]_AT_iem.at;
+#X text 662 173 Normalized Least Mean Square with separated error and
+adaption reference input (linear adaptive FIR-filter);
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+#X text 716 301 energy calculation;
+#X text 46 44 NLMSerr_in~;
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