13th International Conference on Membrane Computing - MTA Sztaki

T. Ahmed, G. DeLancy, A. Paun
generated ux functions [14], [19] did not match the ux functions we assumed
to create time series data. Using these ux functions, we once again preformed
some calculations to determine the time series that these new ux functions
would yield. The results are presented below, adjacent to the most relevant data
for comparison.
4 Results
The following is the presentation of all of the collected results found from applying
the noisy time series data to log-gain theory as well as a plot of the input
time series. Further explanation of the results follows.
Original Functions Flux Functions when SNR = 40
F 0: 0.000002 * A * B * C 4.7101601756476607E-5 + 2.0906741100180677E-6*A*B*C
F 1: 0.00003 * B * C 4.707260180458471E-5 + 3.916897719405373E-5*B*C
F 2: 0.00004 * C * A 6.529552047980467E-8 + 3.91283249939265E-5*C*A
F 3: 0.0005 * B
-1.9008239899554152E-16 + 5.003627980539913E-4*B
F 4:0.007*C
5.464010360230928E-20 + 0.007009755476696823*C
Table 3. SNR 40 Results
Table 4. Error Calculation for an SNR of 40
A B C Average
0.001285799 2.506682785 38281867303 12760622435
Original Functions Flux Functions when SNR = 25
F 0: 0.000002 * A * B * C 0.0012924171688043727 - 1.7117772599596225E-4*A*B*C
F 1: 0.00003 * B * C 0.0012915808119774998 - 0.01727859628397463*B*C
F 2: 0.00004 * C * A 2.1630602392569758E-6 + 7.127342518039588E-6*C*A
F 3: 0.0005 * B
4.0050113261237274E-17 + 5.035262243607753E-4*B
F 4:0.007*C
4.7763864261255335E-21 + 0.0069226436112306364*C
Table 5. SNR 25 Results
As seen above, we calculated percentage of error in all substances and found
the average error. The error shown here between the log-gain generated dynamics
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