199,968 research outputs found
Precision measurement of the structure function ratios F2(He)/F2(D), F2(C)/F2(D) and F2(Ca)/F2(D)
No full textAmaudruz P, Arneodo M, Arvidson A, et al. Precision measurement of the structure function ratios F2(He)/F2(D), F2(C)/F2(D) and F2(Ca)/F2(D). Z.Phys. C. 1991;51(3):387-393
Precision measurement of structure function ratios for Li-6, C-12 and Ca-40
No full textAmaudruz P, Arneodo M, Arvidson A, et al. Precision measurement of structure function ratios for Li-6, C-12 and Ca-40. Z.Phys. C. 1992;53(1):73-77.The structure function ratios F2C/F2Li, F2Ca/F2Li and F2Ca/F2C were measured in deep inelastic muon-nucleus scattering at an incident muon energy of 90 GeV, covering the kinematic range 0.0085 < x < 0.6 and 0.8 < Q2 < 17 GeV2. The sensitivity of the nuclear structure functions to the size and mean density of the target nucleus is discussed
The ratio Fn2/Fp2 in deep inelastic muon scattering
No full textAmaudruz P, Arneodo M, Arvidson A, et al. The ratio Fn2/Fp2 in deep inelastic muon scattering. Nucl.Phys. B. 1992;371(1-2):3-31
Diffraction for non-believers
No full textDiffractive reactions involving a hard scale can be understood in terms of quarks and gluons. These reactions have become a valuable tool for investigating the low-x structure of the proton and the behavior of QCD in the high-density regime, and they may provide a clean environment to study or even discover the Higgs boson at the LHC. In this paper we give a brief introduction to the description of diffraction in QCD. We focus on key features studied in ep collisions at HERA and outline challenges for understanding diffractive interactions at the LHC
Search for light long-lived particles decaying to displaced jets in proton–proton collisions at s = 13.6 TeV
No full textA search for light long-lived particles (LLPs) decaying to displaced jets is presented, using a data sample of proton-proton collisions at a center-of-mass energy of 13.6 TeV, corresponding to an integrated luminosity of 34.7 fb(-1), collected with the CMS detector at the CERN LHC in 2022. Novel trigger, reconstruction, and machine-learning techniques were developed for and employed in this search. After all selections, the observations are consistent with the background predictions. Limits are presented on the branching fraction of the Higgs boson to LLPs that subsequently decay to quark pairs or tau lepton pairs. An improvement by up to a factor of 10 is achieved over previous limits for models with LLP masses smaller than 60 GeV and proper decay lengths smaller than 1 m. The first constraints are placed on the fraternal twin Higgs (FTH) and folded supersymmetry (FSUSY) models, where the lower bounds on the top quark partner mass reach up to 350 GeV for the FTH model and 250 GeV for the FSUSY model
Proton and deuteron F_2 structure functions in deep inelastic muon scattering
No full textAmaudruz P, Arneodo M, Arvidson A, et al. Proton and deuteron F_2 structure functions in deep inelastic muon scattering. Phys.Lett. B. 1992;295(1-2):159-168.The structure functions F2p and F2d measured by deep inelastic muon scattering at incident energies of 90 and 280 GeV are presented. These measurements cover a large kinematic range, 0.006 less-than-or-equal-to x less-than-or-equal-to 0.6 and 0.5 less-than-or-equal-to Q2 less-than-or-equal-to 55 GeV2, and include the first precise data at small x, where large scaling violations are observed. The data agree with earlier results from SLAC and BCDMS but exhibit differences with respect to those of EMC-NA2. Extrapolations to small x of recent phenomenological parton distributions are shown to disagree with the present results
Stairway to discovery: A report on the CMS programme of cross section measurements from millibarns to femtobarns
No full textThe Large Hadron Collider at CERN, delivering proton–proton collisions at much higher energies and far higher luminosities than previous machines, has enabled a comprehensive programme of measurements of the standard model (SM) processes by the CMS experiment. These unprecedented capabilities facilitate precise measurements of the properties of a wide array of processes, the most fundamental being cross sections. The discovery of the Higgs boson and the measurement of its mass became the keystone of the SM. Knowledge of the mass of the Higgs boson allows precision comparisons of the predictions of the SM with the corresponding measurements. These measurements span the range from one of the most copious SM processes, the total inelastic cross section for proton–proton interactions, to the rarest ones, such as Higgs boson pair production. They cover the production of Higgs bosons, top quarks, single and multibosons, and hadronic jets. Associated parameters, such as coupling constants, are also measured. These cross section measurements can be pictured as a descending stairway, on which the lowest steps represent the rarest processes allowed by the SM, some never seen before
Forward produced hadrons in mu p and mu d scattering and investigation of the charge structure of the nucleon
No full textAshman J, Badelek B, Baum G, et al. Forward produced hadrons in Mu p and Mu d scattering and investigation of the charge structure of the nucleon. Z.Phys. C. 1991;52(3):361-387
ANTI-SYNCHRONIZING BACKSTEPPING CONTROL DESIGN FOR ARNEODO CHAOTIC SYSTEM
No full textInternational Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 DOI : 10.5121/ijbb.2013.3103 21 ANTI-SYNCHRONIZING BACKSTEPPING CONTROL DESIGN FOR ARNEODO CHAOTIC SYSTEM Sundarapandian Vaidyanathan1 1Research and Development Centre, Vel Tech Dr. RR & Dr. SR Technical University Avadi, Chennai-600 062, Tamil Nadu, INDIA [email protected] ABSTRACT In this paper, we derive new results for backstepping controller design for the anti-synchronization of Arneodo chaotic system (1980). Backstepping control is a recursive procedure that combines the choice of a Lyapunov function with the design of a feedback controller. In anti-synchronization of chaotic systems, the states of the synchronized systems have the same absolute values, but opposite signs. First, we derive an active backstepping controller for the anti-synchronization of identical Arneodo chaotic systems. Next, we derive an adaptive backstepping controller for the anti-synchronization of identical Arneodo chaotic system, when the system parameters are unknown. The anti-synchronization results for Arneodo chaotic systems have been proved using Lyapunov stability theory. Numerical simulations have been shown to illustrate the backstepping controllers derived in this paper for Arneodo chaotic system. KEYWORDS Backstepping Control; Chaos; Anti-Synchronization; Arneodo System. 1. INTRODUCTION Chaos theory deals with the behaviour of nonlinear dynamical systems that are highly sensitive to initial conditions, an effect which is popularly known as the butterfly effect [1]. Small differences in initial conditions result in widely diverging outcomes for chaotic systems, rending long-term prediction impossible in general. The chaos phenomenon was first observed in weather models by the American scientist, Lorenz ([2], 1963). Since then, chaos theory has found applications in a variety of fields in science and engineering [3-9]. The problem of controlling a chaotic system was first introduced by Ott et al. ([10], 1990). The problem of chaos synchronization occurs when two or more chaotic oscillators are coupled or when a chaotic oscillator drives another chaotic oscillator ([11], 1990). The idea of chaos antisynchronization is to use the output of the master system to control the output of the slave system so that the states of the master and slave systems have the same absolute values, but opposite signs, i.e. the sum of the output signals of the master and slave systems can converge to zero asymptotically. Since the pioneering work by Pecora and Carroll [11], various methods have been developed in the chaos literature for the synchronization of chaotic systems such as active control method [12- 15], adaptive control method [16-20], time-delay feedback control method [21], sampled-data control method [22-23], sliding mode control method [24-30], backstepping control method [31- 33], etc. In this paper, we deploy backstepping control method for the anti-synchronization of identical Arneodo chaotic systems ([34], 1980). Backstepping control method is a recursive procedure that combines the choice of a Lyapunov function with the design of a feedback controller. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 22 The organization of this research paper is as follows. In Section 2, we design an active backstepping controller for the anti-synchronization of identical Arneodo systems when the system parameters are known. In Section 3, we design an adaptive backstepping controller for the anti-synchronization of identical Arneodo systems when the system parameters are unknown. Section 4 contains the conclusions of this work. 2. ACTIVE BACKSTEPPING CONTROLLER DESIGN FOR THE ANTISYNCHRONIZATION OF ARNEODO SYSTEMS 2.1 Theoretical Results Arneodo system ([34], 1980) is one of the classical 3-D chaotic systems as it captures many features of chaotic systems. In this section, we investigate the problem of active backstepping controller design for the anti-synchronization of identical Arneodo chaotic systems, when the system parameters are known. As the master system, we consider the 3-D Arneodo dynamics 1 2 2 3 2 3 1 2 3 1 , , , x x x x x ax bx x x = = = − − − (1) where 1 2 3 x x x , , are the states and a b, are positive, known parameters of the system. Figure 1. Strange Chaotic Attractor of the Arneodo System International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 23 The Arneodo system (1) undergoes chaotic behaviour when the system parameter values are chosen as a = 7.5 and b = 3.8. The strange chaotic attractor of the Arneodo system (1) is shown in Figure 1. As the slave system, we consider the controlled 3-D Arneodo dynamics 1 2 2 3 2 3 1 2 3 1 , , , y y y y y ay by y y u = = = − − − + (2) where 1 2 3 y y y , , are the states and u is the active control to be designed. The anti-synchronization error between the master system (1) and the slave system (2) is defined as 1 1 1 2 2 2 3 3 3 ( ) ( ) ( ), ( ) ( ) ( ), ( ) ( ) ( ). e t y t x t e t y t x t e t y t x t = + = + = + (3) The design problem is to find a control u t( )so that the error converges to zero asymptotically, i.e. ( ) 0 i e t → as t → ∞ for i =1, 2,3. The error dynamics is easily derived as 1 2 2 3 2 2 3 1 2 3 1 1 , , . e e e e e ae be e y x u = = = − − − − + (4) In this section, we apply the active backstepping control method to design a controller u t( ). Theorem 1. The identical Arneodo chaotic systems (1) and (2) are globally and exponentially anti-synchronized for all initial conditions by the active backstepping controller 2 2 1 2 3 1 1 u t a e b e e y x ( ) (3 ) (5 ) 2 . = − + − − − + + (5) Proof. First, we define a Lyapunov function 2 1 1 1 , 2 V z = (6) where 1 1 z e = . (7) Its time derivative along the solutions of systems (1) and (2) is obtained as 2 1 1 1 1 1 1 2 1 1 1 2 V z z e e e e z z e e = = = = − + + ( ). (8) International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 24 Next, we define 2 1 2 z e e = + . (9) From (9), it follows that 2 1 1 1 2 V z z z = − + . (10) Secondly, we define the Lyapunov function ( ) 2 2 2 2 1 2 1 2 1 1 . 2 2 V V z z z = + = + (11) The time derivative of V2 is given by 2 2 2 1 2 2 1 2 3 V z z z e e e = − − + + + (2 2 ). (12) Next, we define 3 1 2 3 z e e e = + + 2 2 . (13) From (13), it follows that 2 2 2 1 2 2 3 V z z z z = − − + . (14) Finally, we define the Lyapunov function ( ) 2 2 2 2 2 3 1 2 3 1 1 . 2 2 V V z z z z = + = + + (15) Clearly, V is a positive definite function on 3 R . The time derivative of V is obtained as ( ) 2 2 2 2 1 2 2 3 3 2 3 1 2 3 1 1 V z z z z z e e ae be e y x u = − − + + + + − − − − + 2 2 (16) A simple calculation gives 2 2 2 2 2 1 2 3 3 1 2 3 1 1 V z z z z a e b e e y x u = − − − + + + − + − − + (3 ) (5 ) 2 . (17) Substituting the backstepping controller u defined by (5) in (17), we get 2 2 2 1 2 3 V z z z = − − − . (18) Clearly, V is a negative definite function on 3 R . Hence, by Lyapunov stability theory [35], the error dynamics (4) is globally exponentially stable. This completes the proof. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 25 2.2 Numerical Results For numerical simulations using MATLAB, the fourth order Runge-Kutta method with initial step 8 h 10− = is used to solve the Arneodo systems (1) and (2) with the backstepping controller u defined by (5). The parameters of the Arneodo chaotic systems are selected as a = 7.5 and b = 3.8. The initial values of the master system (1) are chosen as 1 2 3 x x x (0) 14, (0) 5, (0) 6 = = − = The initial values of the slave system (2) are chosen as 1 2 3 y y y (0) 18, (0) 12, (0) 16 = = = − Figure 2 depicts the anti-synchronization of Arneodo chaotic systems (1) and (2). Figure 3 depicts the time-history of the anti-synchronization errors 1 2 3 e e e , , . Figure 2. Anti-Synchronization of Arneodo Chaotic Systems International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 26 Figure 3. Time-History of the Anti-Synchronizing Errors 1 2 3 e e e , , 3. REGULATING ACTIVE BACKSTEPPING CONTROLLER DESIGN FOR THE ANTI-SYNCHRONIZATION OF ARNEODO SYSTEMS 3.1 Theoretical Results In this section, we derive new results for the adaptive backstepping controller design for antisynchronization of Arneodo systems when the parameters a and b are unknown. As the master system, we consider the 3-D Arneodo dynamics 1 2 2 3 2 3 1 2 3 1 , , , x x x x x ax bx x x = = = − − − (19) where 1 2 3 x x x , , are the states and a b, are unknown parameters of the system. As the slave system, we consider the controlled 3-D Arneodo dynamics 1 2 2 3 2 3 1 2 3 1 , , , y y y y y ay by y y u = = = − − − + (20) where 1 2 3 y y y , , are the states and u is the adaptive control to be designed. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 27 The anti-synchronization error between the master system (19) and the slave system (20) is defined as 1 1 1 2 2 2 3 3 3 ( ) ( ) ( ), ( ) ( ) ( ), ( ) ( ) ( ). e t y t x t e t y t x t e t y t x t = + = + = + (21) The design problem is to find a control u t( )so that the error converges to zero asymptotically, i.e. ( ) 0 i e t → as t → ∞ for i =1, 2,3. The error dynamics is easily derived as 1 2 2 3 2 2 3 1 2 3 1 1 , , . e e e e e ae be e y x u = = = − − − − + (22) In this section, we apply the adaptive backstepping control method to design a controller u t( ). Inspired by the control law defined by Eq. (5) in the active backstepping controller design, we may consider the adaptive backstepping controller design law given by 2 2 1 2 3 1 1 ˆ u t a e b e e y x ( ) (3 ) (5 ) 2 , = − + − − − + + ˆ (23) where a t ˆ( ) and ˆb t( ) are estimates of the unknown parameters a and b,respectively. We define the parameter estimation errors as ( ) ( ) ˆ a e t a a t = − and ˆ ( ) ( ) b e t b b t = − (24) Note that ( ) ( ) ˆ a e t a t = − and ˆ ( ) ( ) b e t b t = − (25) Next, we shall state and prove the second main result of this paper. Theorem 2. The identical Arneodo chaotic systems (19) and (20) with unknown parameters a and b are globally and exponentially anti-synchronized for all initial conditions by the adaptive backstepping controller 2 2 1 2 3 1 1 ˆ u t a e b e e y x ( ) (3 ) (5 ) 2 , = − + − − − + + ˆ (26) where a t ˆ( ) and ˆb t( ) are estimates of a and b,respectively, and the parameter update law is given by 1 2 3 1 1 2 3 2 ˆ( ) (2 2 ) , ˆ ( ) (2 2 ) , a a b b a t e e e e k e b t e e e e k e = + + + = − + + + (27) International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 28 with positive control gains a k and . b k Proof. First, we define the Lyapunov function 2 1 1 1 , 2 V z = (28) where 1 1 z e = . (29) The time derivative of V1 is given by 2 1 1 1 1 1 1 2 1 1 1 2 V z z e e e e z z e e = = = = − + + ( ). (30) Next, we define 2 1 2 z e e = + . (31) From (30), it follows that 2 1 1 1 2 V z z z = − + . (32) Secondly, we define the Lyapunov function ( ) 2 2 2 2 1 2 1 2 1 1 . 2 2 V V z z z = + = + (33) The time derivative of V2 is given by 2 2 2 1 2 2 1 2 3 V z z z e e e = − − + + + (2 2 ). (34) Next, we define 3 1 2 3 z e e e = + + 2 2 . (35) From (34), it follows that 2 2 2 1 2 2 3 V z z z z = − − + . (35) Finally, we define the Lyapunov function ( ) ( ) 2 2 2 2 2 2 2 2 2 3 1 2 3 1 1 1 . 2 2 2 V V z e e z z z e e = + + + = + + + + a b a b (36) The time derivative of V is obtained as 2 2 2 2 2 1 2 3 3 1 2 3 1 1 ˆ (3 ) (5 ) 2 . ˆ V z z z z a e b e e y x u e a e b a b = − − − + + + − + − − + − − (37) Substituting the backstepping controller u defined by (26) in (37), we get ( ) ( ) 2 2 2 1 2 3 1 3 2 3 ˆ ˆ . V z z z e e z a e e z b = − − − + − + − − a b (38) International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 29 Substituting the parameter law (27) in (38) and noting that 3 1 2 3 z e e e = + + 2 2 , we get 2 2 2 2 2 1 2 3 , V z z z k e k e = − − − − − a a b b (39) which is a negative definite function on 5 R . Thus, by Lyapunov stability theory [35], the proof is complete. 3.2 Numerical Results For numerical simulations with MATLAB, the fourth-order Runge-Kutta method with initial step 8 h 10− = is used to solve the Arneodo systems (19) and (20) with the backstepping controller u defined by (26) and the parameter update law defined by (27). The parameters of the Arneodo chaotic systems are chosen as a = 7.5 and b = 3.8. The initial values of the parameter estimates are chosen as aˆ(0) 16 = and ˆb(0) 9. = The control gains are chosen as 6 a k = and 6. b k = The initial values of the master system (19) are chosen as 1 2 3 x x x (0) 4, (0) 5, (0) 8 = = = − The initial values of the slave system (20) are chosen as 1 2 3 y y y (0) 2, (0) 6, (0) 5 = = = − Figure 4 depicts the anti-synchronization of Arneodo chaotic systems. Figure 5 depicts the timehistory of the anti-synchronization errors 1 2 3 e e e , , .Figure 6 depicts the time-history of the parameter estimation errors , . a b e e International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 30 Figure 4. Anti-Synchronization of Arneodo Chaotic Systems Figure 5. Time-History of the Anti-Synchronization Errors 1 2 3 e e e , , International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 31 Figure 6. Time-History of the Parameter Estimation Errors , a n e e 4. CONCLUSIONS In this paper, we derived new results for the anti-synchronization of identical Arneodo chaotic systems (1980) via backstepping control method. First, active backstepping controller was designed for the anti-synchronization of identical Arneodo chaotic systems with known system parameters. Next, adaptive backstepping controller was designed for the anti-synchronization of identical Arneodo chaotic systems with unknown system parameters. All the stability results in this paper were established using Lyapunov stability theory. Numerical figures using MATLAB were shown to illustrate the validity and effectiveness of the backstepping controller design for the anti-synchronization of identical chaotic systems for both the cases of known and unknown system parameters. REFERENCES [1] Alligood, K.T., Sauer, T. & Yorke, J.A. (1997) Chaos: An Introduction to Dynamical Systems, Springer Verlag, New York. [2] Lorenz, E.N. (1963) “Deterministic non-periodic flow,” Journal of the Atmospheric Sciences, Vol. 20, pp 130-141. [3] Lakshmanan, M. & Murali, K. (1996) Nonlinear Oscillators: Controlling and Synchronization, World Scientific, Singapore. [4] Petrov, V., Gaspar, V., Masere, J. & Showalter, K. (1993) “Controlling chaos in the BelousovZhabotinsky reaction,” Nature, Vol. 361, pp 240-243. [5] Kuramoto, Y. (1984) Chemical Oscillations, Waves and Turbulence, Springer Verlag, New York. [6] Van Wiggeren, G. & Roy, R. (1998) “Communicating with chaotic lasers,” Science, Vol. 279, pp 1198-1200. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 32 [7] Garfinkel, A., Spano, M.L., Ditto, W.L. & Weiss, J.A. (1992) “Controlling cardiac chaos,” Science, Vol. 257, pp 1230-1235. [8] Blasius, B., Huppert, A. & Stone, L. (1999) “Complex dynamics and phase synchronization in spatially extended ecological system,” Nature, Vol. 399, pp 354-359. [9] Cuomo, K.M. & Oppenheim, A.V. (1993) “Circuit implementation of synchronized chaos with applications to communications,” Physical Review Letters, Vol. 71, No. 1, pp 65-68. [10] Ott, E., Grebogi, C. & Yorke, J.A. (1990) “Controlling chaos,” Physical Review Letters, Vol. 64, pp 1196-1199. [11] Pecora, L.M. & Carroll, T.L. (1990) “Synchronization in chaotic systems,” Physical Review Letters, Vol. 64, pp 821-824. [12] Chen, H.K. (2005) “Global chaos synchronization of new chaotic systems via nonlinear control,” Chaos, Solitons & Fractals, Vol. 23, pp 1245-1251. [13] Lu, L., Zhang, C. & Guo, Z.A. (2007) “Synchronization between two different chaotic systems with nonlinear feedback control,” Chinese Physics, Vol. 16, No. 6, pp 1603-1607. [14] Sundarapandian, V. (2011) “Global chaos synchronization of Shimizu-Morioka and Liu-Chen chaotic systems by active nonlinear control,” International Journal of Advances in Science and Technology, Vol. 2, pp 11-20. [15] Sundarapandian, V. & Karthikeyan, R. (2011) “Hybrid chaos synchronization of hyperchaotic Lorenz and hyperchaotic Chen systems by active non-linear control,” International Journal of Electrical and Power Engineering, Vol. 5, No. 5, pp 186-192. [16] Chen, S.H. & Lü, J. (2002) “Synchronization of an uncertain unified system via adaptive control,” Chaos, Solitons & Fractals, Vol. 14, pp 643-647. [17] Sundarapandian, V. (2011) “Adaptive synchronization of hyperchaotic Lorenz and hyperchaotic Lü systems,” International Journal of Instrumentation and Control Systems, Vol. 1, pp 1-18. [18] Sundarapandian, V. & Karthikeyan, R. (2011) “Anti-synchronization of Lü and Pan chaotic systems by adaptive nonlinear control,” European Journal of Scientific Research, Vol. 64, No. 1, pp 94-106. [19] Sundarapandian, V. & Pehlivan, I. (2012) “Analysis, control, synchronization and circuit design of a novel chaotic system,” Mathematical and Computer Modelling, Vol. 55, pp 1904-1915. [20] Sundarapandian, V. & Karthikeyan, R. (2012) “Global chaos synchronization of hyperchaotic Pang and hyperchaotic Wang systems via adaptive control, International Journal of Soft Computing, Vol. 7, No. 1, pp 28-37. [21] Park, J.H. & Kwon, O.M. (2005) “A novel criterion for delayed feedback control of time-delay chaotic systems,” Chaos, Solitons & Fractals, Vol. 23, pp 495-501. [22] Yang, T. & Chua, L.O. (1999) “Control of chaos using sampled-data feedback control,” International Journal of Bifurcation and Chaos, Vol. 9, pp 215-219. [23] Lee, S.H., Kapila, V., Porfiri, M. & Panda, A. (2010) “Master-slave synchronization of continuously and intermittently coupled sampled-data chaotic oscillators,” Communications in Nonlinear Science and Simulation, Vol. 15, pp 4100-4113. [24] Yau, H.T. (2008) “Chaos synchronization of two uncertain chaotic nonlinear gyros using fuzzy sliding mode control,” Mechanical Systems and Signal Processing, Vol. 22, No. 2, pp 408-418. [25] Noroozi, N., Roopaei, M. & Jahromi, M.Z. (2009) “Adaptive fuzzy sliding mode control scheme for uncertain systems,” Vol. 14, No. 11, pp 3978-3992. [26] Sundarapandian, V. (2011) “Global chaos synchronization of Pan systems by sliding mode control,” International Journal of Embedded Systems, Vol. 1, pp 41-50. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 33 [27] Sundarapandian, V. & Sivaperumal, S. (2012) “Sliding mode controller design for hybrid synchronization of hyperchaotic Chen systems,” International Journal of Computational Sciences and Applications, Vol. 2, No. 1, pp. 35-47. [28] Sundarapandian, V. & Sivaperumal, S. (2012) “Sliding mode controller for global chaos synchronization of Coullet systems,” International Journal of Information Science & Techniques, Vol. 2, No. 2, pp. 65-76. [29] Sundarapandian, V. & Sivaperumal, S. (2012) “Anti-synchronization of four-wing chaotic systems via sliding mode control,” International Journal of Automation and Computing, Vol. 9, No. 3, pp. 274-279. [30] Sundarapandian, V. (2012) “Sliding controller design for the hybrid chaos synchronization of identical hyperchaotic Xu systems,” International Journal of Instrumentation and Control Systems, Vol. 2, No. 4, pp 61-71. [31] Yu, Y.G. & Zhang, S.C. (2006) “Adaptive backstepping synchronization of uncertain chaotic systems,” Chaos, Solitons & Fractals, Vol. 27, pp 1369-1375. [32] Suresh, R. & Sundarapandian, V. (2012) “Global chaos synchronization of WINDMI and Coullet chaotic systems by backstepping control,” Far East Journal of Mathematical Sciences, Vol. 67, No. 2, pp 265-287. [33] Suresh, R. & Sundarapandian, V. (2012) “Hybrid synchronization of n-scroll Chua and Lur’e chaotic systems via backstepping control with novel feedback,” Archives of Control Sciences, Vol. 22, No. 3, pp 255-278. [34] Arneodo, A., Coullet, P. & Tresser, C. (1980) “Occurrence of strange attractors in threedimensional Volterra equations,” Physics Letters A, Vol. 79, pp 259-263. [35] Hahn, W. (1967) The Stability of Motion, Springer Verlag, New York. Author Dr. V. Sundarapandian earned his Doctor of Science degree in Electrical and Systems Engineering from Washington University, St. Louis, USA in May 1996. He is Professor and Dean of the Research and Development Centre at Vel Tech Dr. RR & Dr. SR Technical University, Chennai, Tamil Nadu, India. He has published over 290 papers in refereed international journals. He has published over 180 papers in National and International Conferences. He is an Indian Chair of AIRCC. He is the Editor-in-Chief of the AIRCC Journals – IJICS, IJCTCM, IJITCA, IJCCMS and IJITMC. He is the Editor-in-Chief of the Wireilla Journals - IJSCMC, IJCBIC, IJCSITCE, IJACEEE and IJCCSCE. He is an associate editor of many international journals on Computer Science, IT and Control Engineering. His research interests are Linear and Nonlinear Control Systems, Chaos Theory and Control, Soft Computing, Optimal Control, Operations Research, Mathematical Modelling and Scientific Computing. He has delivered several Key Note Lectures on Control Systems, Chaos Theory, Scientific Computing, Mathematical Modelling, MATLAB and SCILAB
Quasielastic J/psi muoproduction from hydrogen, deuterium, carbon and tin
No full textArneodo M, Arvidson A, Badelek B, et al. Quasielastic J/psi muoproduction from hydrogen, deuterium, carbon and tin. Phys.Lett. B. 1994;332(1-2):195-200
- …
