Rasband, S. N., Chaotic Dynamics of Non-Linear Systems. Chichester: Wiley Interscience, 1990. Home/Search Document Not in Database Summary Related Articles Check |
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Soft Computing for Intelligent Control of Nonlinear Dynamical .. - Melin, Castillo (2004) (Correct)
....advantages of neural networks (learning and adaptability) with the advantages of fuzzy logic (use of expert knowledge) to achieve the goal of robust adaptive control of robotic dynamic systems. We consider that our method for adaptive control can be applied to general non linear dynamical systems [8, 27] because the hybrid approach, combining neural networks and fuzzy logic, does not depend on the particular characteristics of the robotic dynamic systems. The new method for adaptive control can also be applied for autonomous robots [8] but in this case it may be necessary to include genetic ....
.... expressed as di#erential (or di#erence) equations [3, 17, 18] Now we propose mathematical models that integrate our method for geometrical modelling of bacteria growth using the fractal dimension [14] with the method for modelling the dynamics of bacteria population using di#erential equations [27]. The resulting mathematical models describe bacteria growth in space and in time, because the use of the fractal dimension enables us to classify bacteria by the geometry of the colonies and the di#erential equations help us to understand the evolution in time of bacteria population. We will ....
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Rasband, S.N. (1990). Chaotic Dynamics of Non-Linear Systems, John Wiley & Sons.
Problematic Likelihood Functions from Sensible Population.. - Givens, Poole (Correct)
....resulting from the dynamics model discussed here; model variations and additional model complexity do little to mitigate these problems. The primary cause of these problems is that the model and historical catch data together establish a nearly chaotic dynamical nonlinear system in the sense of Rasband (1990). Bowhead abundances are projected with self referencing dynamics over a long uninterrupted historical period without reference to external data. This allows tiny changes in the early portion of the trajectory to cause huge changes in recent years, and it is only the recent years for which we have ....
Rasband, S. N., 1990. Chaotic Dynamics of Nonlinear Systems. John Wiley and Sons, New York, 230pp.
Neural Network Modeling of Discrete-Time Chaotic Maps - Homayoun Yousefi'zadeh (Correct)
....them quite suitable for our time series prediction task. An outline of the paper follows. In section [2] we briefly review the qualitative behavior of discretetime transformations. In section [3] we study the perceptron neural networks and the back propagation learning algorithm. In section [4], we explain how to model discrete time dynamical transformations with neural networks. In section [5] we summarize the experimental findings. 2 Qualitative Behavior of Discrete Time Maps Consider a real closed interval I = a; b] and a nonlinear function f( which transforms any point x of I ....
S.N.Rasband, "Chaotic Dynamics of Nonlinear Systems.", J.Wiley & Sons Co. 1989.
A Dynamical Adaptive Resonance Architecture - Gregory Heileman (Correct)
....network architecture, with appropriate interconnections and describing equations, which as a whole exhibits pattern clustering capabilities. Second, the analysis of dynamical systems is a well understood and rich area as witnessed by the recent growth in the sciences of chaos and nonlinear physics [3, 4]. A dynamical system setting, where a neural network is allowed to follow a trajectory set by the initial conditions and the external inputs, is a natural medium for studying the stability, structure, and capabilities of a network [5] Moreover, such a setting allows us to generalize a particular ....
S. Neil Rasband. Chaotic Dynamics of Nonlinear Systems. Wiley, New York, 1990.
Period-Doublings to Chaos in A Simple Neural Network: An.. - Wang (1991) (11 citations) (Correct)
.... 1; 2; 2 2 ; 2 3 Delta 5; 2 3 Delta 3; 2 2 Delta 5; 2 2 Delta 3; 2 Delta 5; 2 Delta 3; 7; 5; 3: ffl The sequences 2 n and 2 n converge [5, 15] from below and above respectively, to a parameter value ( 3:5699456: for the quadratic family [15, 32]) at the universal rate ffi = lim n 1 n Gamma n 1 n 1 Gamma n 2 = lim n 1 n Gamma n 1 n 1 Gamma n 2 = 4:669 Delta Delta Delta The ffi is also known as the Feigenbaum number, which has been analytically studied by Sullivan [38] using quasiconformal ....
S. N. Rasband. Chaotic Dynamics of Nonlinear Systems. John Wiley & Son, Inc., 1990.
Self-Organization, Emergence and Multi-Agent Systems - Gabbai, Yin, Wright, Allinson (2005) (Correct)
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Rasband, S. N., Chaotic Dynamics of Non-Linear Systems. Chichester: Wiley Interscience, 1990.
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