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Theory of Linear and nonlinear circuits - Engberg J.

Engberg J. Theory of Linear and nonlinear circuits - Wiley & sons , 1995. - 154 p.
ISBN 0-47-94825 Previous << 1 .. 56 57 58 59 60 61 < 62 > 63 64 65 66 67 68 .. 85 >> Next where the contribution to the controlling variable due to the noise sources has been ignored (low level noise is assumed). Once the controlling variable u2ll(0 has been determined the response r–≥t) can be determined as
210
S. Noise in non-linear systems: Examples and Conclusion
ri{t) ‚Äî t^ifO ¬± ¬± ¬± ^i(0 + n2(t)\ R
+ 02 [si(t) + ni(t) + n2(t)]2 4- a3[s](t) + m(t) + –ø2(–≥)13
(8.146)
As a special case it is quite interesting to note that the autocorrelation in the situation where the system is linear‚Äî but including the (non-linear) modulation of noise source 2 ‚Äî is given by
which can be derived by setting a2 = a-j = 0.
Simulation 1 The autocorrelation (j'rI,.il(¬£p)|2)f with p = 3 is simulated versus the number of iterations –ì. The data and result of the simulation are shown in Figure 8.12. This simulation is for a linear system, i.e. a2 = a3 = 0, but where noise source 2 is being modulated by the applied deterministic signal. The simulated result = 1.7523 X 10_1¬∞ agrees with the theoretical
result (|–≥–ø,1(¬£–∑)|2) = 1.7596 X 10_1¬∞ with a deviation of -0.42 %.
Simulation 2 The autocorrelation {|, 1 (cfp)|2)–∑–¥—ç–æ? with p = 3 is simulated versus tlie amplitude 0\t\ of the deterministic input sinusoidal signal. The data and result of the simulation are shown in Figure 8.13. All simulated values (|–≥–∞–¥(¬£–∑)|2)–∑–¥9¬ß¬∞ agree with the theoretical result (|rnil(f3)|2) in Equation
(8.144) with a deviation less than ¬±0.39 %. As seen from Figure 8.12 the noise level (|^i(¬£t)|2) increases with the amplitudes = 0-1 9 of the deterministic excitation signals. For very small only noise source 1 is of importance since it, as opposed to noise source 2, is independent of a modulating signal.
Simulation 3 The autocorrelation (|?–ª.1(¬£—Ä)|2)–∑¬∞—è–ø? p = 3 is simulated versus the standard deviations 0\ ‚Äî <j2. The data and result of the simulation are shown in Figure 8.14. All simulated values (|–≥*,|({–∑)|–≥)—è99–æ? 'ri the range of ai = rr< from 10-9 to 10-2 agree with the theoretical result (|–≥,–¥((–∑)|2) in Equation (8.144) with deviations less than ¬±0.14 %. The simulation for –Ω\ = n2 ‚Äî 10-i deviates from the theoretical result by u.ii %. Tins rather large deviation is due to the fact that the mixing of noise with noise for increasing standard deviations is not considered in the theoretical prediction but is included in the numerical simulation.
(I^a(?p)|2) = (Rl + R)2C'i (–®–´\3)
(8.147)
8.3. Example 3
‚ñÝ211
1.85
1.80
1.75 1.70 1.65 1.60 1.55
0 5000 10000 15000 20000 25000 30000 35000 40000
Number of iterations, –ì
Figure 8.12: Example 3: A numerical experiment to illustrate the properties of noise response rn,i(¬£p). The full line gives the simulated values {\rn–¥(^)|2)–≥ versus number of iterations, and the dotted line is the theoretical result (|–≥–∞–î(^—Ä))2) predicted from Equation
(8.144). The simulated (^–Ý)12)–∑—ç¬ß–æ? ‚Äî 1-7523 x 10"iO with a standard deviation of 2.7810 x 10'19. The theoretical result is (!ni(¬£i7)P) = 1.7596 x 10-15. This corresponds to a deviation of the simulated result from the theoretical result of ‚Äî0.42 %. The data for the experiment are: –õ - 64, = 5.0 x 10-9. ¬´—Ç3 ‚Äî 7.0 x if)-*, C\ ~ 0.3, C~ ~ 0.2, /?, 13.0,
R = 10.3. a2 = 0, a3 = 0; p = 3; r = 0 5, <?‚Ä¢.. = 0.2, - 4.0; and ^ , = 0.0.
'212 8. Noise in non-linear systems: Examples and Conclusion *
Amplitude, Qi
Figure 8.13: Example 3: A numerical experiment ro illustrate the properties of noise response rnil(^). The full line gives the theoretical values for (!^n,iUp)|2) versus the amplitude of the input signal, and the circles give the simulated values {|–≥–ø–¥(—á—Ä)|2)–∑—ç—ç–æ1 predicted from Equation (8.144). The simulated values agree with the theoretical predictions with a deviation less than ¬±0.39 % for the amplitudes seen from the figure. The data for the experiment are: –õ ‚Äî 64, = 5.4 x 10‚Äú‚Äú, <t2 = 7.3 x 10~e. C\ = 0.3o, Co = 0/27, R{ = 13.1,
R ‚Äî 10.3, 0.2 ‚Äî 27.4, –∞–∑ ‚Äî 76.9, p ~ 3, r ‚Äî 0.5, = 4.0, -md pi i ‚Äî 0.0
8.3. Example 3
213
Standard deviation, ‚Äî —Å–≥2
Figure 8.14: Example 3: A numerical experiment to illustrate the properties of noise response –≥–ø–¥(¬£–Ý)- The full lino gives the theoretical vaiues for (|–≥–ø–¥(¬£—Ä)|2) versus the s-tandard deviations of the noise sources cri = rr2) and the circles give the simulated vaiues \|–≥–ø–¥(—á—Ä)|2)–∑—ç9–æ? predicted from Equation (8.144). The simulated values agree with the theoretical predictions with deviations less than ¬±0.17 % for standard deviations up to 10~–≥. For <7\ = cr2 = 10‚Äù1 the deviation between simulation and theory is 6.0 %. The data for the experiment are: A = 64. C\ ‚Äî 0.33. C-j. ~ 0.27. Ft-. ~ 13.1. Ft ‚Äî 10.3. a 2 = 27 4, = 76-9,
p ‚Äî 3, r = 0.5, t9i(i = 4.0, and = 0.0.
214
S. Noise in non-linear systems: Examples and Conclusion
8.4 Conclusion
A method has been presented to analyze noise in non-autonomous non-linear multi-port networks and systems where low level noise can be assumed. This means that the systems must be small signal linear which is true lor many types of systems. Previous << 1 .. 56 57 58 59 60 61 < 62 > 63 64 65 66 67 68 .. 85 >> Next 