13th International Conference on Membrane Computing - MTA Sztaki

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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 76
A case-study on the influence of noise to log-gain principles for flux dynamic discovery Fig. 4. Noisy time series of A when SNR = 40 Fig. 5. Time series of A calculated from the ux dynamics given by log-gain theory Fig. 6. Noisy time series of Bwhen SNR = 40 Fig. 7. Time series of Bcalculated from the ux dynamics given by log-gain theory Fig. 8. Noisy time series of C when SNR = 40 Fig. 9. Time series of C calculated from the ux dynamics given by log-gain theory 77
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Erzsébet Csuhaj-Varjú Marian Gheo
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Editors Erzsébet Csuhaj-Varjú Dep
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Preface In addition to the texts or
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Contents M.A. Martínez-del-Amor, I
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(Tissue) P systems with decaying ob
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B. Aman, G. Ciobanu formalisms is e
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B. Aman, G. Ciobanu membranes appea
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B. Aman, G. Ciobanu • [ ] recep r
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B. Aman, G. Ciobanu Definition 3. A
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B. Aman, G. Ciobanu { w i , for all
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B. Aman, G. Ciobanu The four transi
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B. Aman, G. Ciobanu A state space i
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B. Aman, G. Ciobanu to reiterate th
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On structures and behaviors of spik
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L. Cienciala, L. Ciencialová, M. P
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H. ElGindy, R. Nicolescu, H. Wu pat
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H. ElGindy, R. Nicolescu, H. Wu pro
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H. ElGindy, R. Nicolescu, H. Wu (b)
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H. ElGindy, R. Nicolescu, H. Wu 8 r
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H. ElGindy, R. Nicolescu, H. Wu Pse
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H. ElGindy, R. Nicolescu, H. Wu to
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H. ElGindy, R. Nicolescu, H. Wu ter
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H. ElGindy, R. Nicolescu, H. Wu 4.
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H. ElGindy, R. Nicolescu, H. Wu Tab
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H. ElGindy, R. Nicolescu, H. Wu 6 R
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H. ElGindy, R. Nicolescu, H. Wu (wh
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A formal framework for P systems wi
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A new approach for solving SAT by P
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Maintenance of chronobiological inf
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4 3.5 3 2.5 2 1.5 1 0.5 0 0 50 100
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Using a kernel P system to solve th
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Spiking neural P systems with funct
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L.F. Macías-Ramos, M.J. Pérez-Jim
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M.A. Martínez-del-Amor, I. Pérez-
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Membranes with local environments A
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Membranes with local environments T
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Membranes with local environments -
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Membranes with local environments -
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Membranes with local environments I
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On efficient algorithms for SAT dis
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On efficient algorithms for SAT 2 S
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On efficient algorithms for SAT 2.4
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On efficient algorithms for SAT inc
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On efficient algorithms for SAT •
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On efficient algorithms for SAT Not
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On efficient algorithms for SAT the
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On efficient algorithms for SAT 32.
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A. Obtu̷lowicz - its underlying tr
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A. Obtu̷lowicz 2−ramification
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A. Obtu̷lowicz The correctness of
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A. Obtu̷lowicz The arcs (links) fr
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An analysis of correlative and quan
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Sublinear-space P systems with acti
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Modelling ecological systems with t
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D. Sburlan hence one can gather use
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D. Sburlan represents a 0L system.
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D. Sburlan assuming that M is in a
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D. Sburlan q 1 {t−, T 1 ↓, T 2
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D. Sburlan In case of M, the states
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D. Sburlan We introduced a formal s
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P. Sosík [9, 10], several research
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P. Sosík The rules of a system lik
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P. Sosík 1. The family Π is polyn
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P. Sosík (a) subsequently and recu
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P. Sosík contentFinal = content(l,
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P. Sosík Hence, with the aid of th
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P. Sosík 8. Lakshmanan K. and Rama
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S. Verlan, J. Quiros more relevance
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S. Verlan, J. Quiros For a regular
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S. Verlan, J. Quiros digital FPGA c
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S. Verlan, J. Quiros where k j = N
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S. Verlan, J. Quiros Now let us con
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S. Verlan, J. Quiros We consider a
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S. Verlan, J. Quiros the present co
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S. Verlan, J. Quiros Table 2 gives
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S. Verlan, J. Quiros Using similar
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S. Verlan, J. Quiros 5. M. Maliţa,
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Simplifying Event-B models of P sys
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Author Index Adorna H., 143 Agrigor
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13th International Conference on Membrane Computing - MTA Sztaki

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