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+#X text 22 41 internal downsampling of update;
+#X msg 75 339 gamma \$1;
+#X text 90 365 input signal;
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+#X text 704 47 (array-sizes have to be >= then FIR_size);
+#X text 654 37 1.arg: <float> number of order of FIR-filter;
+#X text 57 73 update every sample;
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+#X text 57 88 update every 2nd sample;
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+#X text 19 397 filtered output signal;
+#X text 339 396 error signal;
+#X text 469 457 constrain;
+#X text 469 467 coefficients;
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+#X text 243 146 x[n-i];
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+#X text 223 130 _________________________________;
+#X text 6 135 normalized learn rate:;
+#X text 173 54 y(n) = W * x(n);
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+#X text 119 73 error:;
+#X text 116 55 output:;
+#X text 7 212 coefficient iteration:;
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+#X text 41 365 x1(n);
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+#X text 572 3 Multi Channel Constraint;
+#X msg 463 139 \; 1_W const 0 \; 2_W const 0 \; 3_W const 0;
+#X text 462 117 RESET TABLES;
+#X text 654 150 (C) 2005 \, m.noisternig & t.musil \, IEM \, Graz \,
+Austria;
+#X text 682 163 [noisternig \, musil]_AT_iem.at;
+#X text 655 64 2.arg: <float> number of order of FIR-filter;
+#X text 655 78 3.arg: <float> learn-rate = beta;
+#X text 656 103 5.arg: <float> constraint = kappa;
+#X text 655 90 4.arg: <float> regularization parameter = gamma;
+#X text 656 118 6.arg: <symbol> table-name of W;
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