Nonlinear system analysis

XPPAUT (http //www.math.pitt.edu/ bard/xpp/xpp.html) offers deterministic simulations with a set of very good stiff solvers. It also offers fitting, stability analysis, nonlinear systems analysis, and time-series analysis, like histograms. The GUI is simple. It is mainly available under Linux, but also mns on Windows. 

Sastry Nonlinear Systems Analysis, Stability, and Control... 

Vidyasagar, M., Nonlinear Systems Analysis, 2nd ed. Prentice Hall, 1993. 

Experimental NMR data are typically measured in response to one or more excitation pulses as a function of the time following the last pulse. From a general point of view, spectroscopy can be treated as a particular application of nonlinear system analysis [Bogl, Deul, Marl, Schl]. One-, two-, and multi-dimensional impulse-response functions are defined within this framework. They characterize the linear and nonlinear properties of the sample (and the measurement apparatus), which is simply referred to as the system. The impulse-response functions determine how the excitation signal is transformed into the response signal. A nonlinear system executes a nonlinear transformation of the input function to produce the output function. Here the parameter of the function, for instance the time, is preserved. In comparison to this, the Fourier transformation is a linear transformation of a function, where the parameter itself is changed. For instance, time is converted to frequency. The Fourier transforms of the impulse-response functions are known to the spectroscopist as spectra, to the system analyst as transfer functions, and to the physicist as dynamic susceptibilities. 

French, A.S. 1976. Practical nonlinear system analysis by Wiener kernel estimation in the frequency domain. Biol. Cybern. 24 111. 

Sutter, E. 1992. A deterministic approach to nonlinear systems analysis. In R.B. Pinter and B. Nabet (Eds.), Nonlinear Vision, pp. 171 220, Boca Raton, FL, CRC Press. 

Hedrick, J.K., Paynter, H.M., eds. (1976). Nonlinear system analysis and synthesis. ASME ... 

Sasty, S. 1999. Nonlinear Systems. Analysis, Stability, and Control. Springer. New York. 

Berger T.W., J.L. Eriksson, D.A. Ciarolla, and R.J. Sclabassi 1988. Nonlinear systems analysis of the hippocampal perforant path-dentate projection. /. Neurophysiol. 60 1077. 

General first-order kinetics also play an important role for the so-called local eigenvalue analysis of more complicated reaction mechanisms, which are usually described by nonlinear systems of differential equations. Linearization leads to effective general first-order kinetics whose analysis reveals infomiation on the time scales of chemical reactions, species in steady states (quasi-stationarity), or partial equilibria (quasi-equilibrium) [M, and ]. 

In his paper On Governors , Maxwell (1868) developed the differential equations for a governor, linearized about an equilibrium point, and demonstrated that stability of the system depended upon the roots of a eharaeteristie equation having negative real parts. The problem of identifying stability eriteria for linear systems was studied by Hurwitz (1875) and Routh (1905). This was extended to eonsider the stability of nonlinear systems by a Russian mathematieian Lyapunov (1893). The essential mathematieal framework for theoretieal analysis was developed by Laplaee (1749-1827) and Fourier (1758-1830). 

Allocation of labor, 297 Allocation of weapons, 291 Amplitude, covariant physical interpretation, 535 Analysis, numerical, 50 Analysis, parexic, 52 Analytical methods for nonlinear systems, 349... 

The partial differential equations describing the catalyst particle are discretized with central finite difference formulae with respect to the spatial coordinate [50]. Typically, around 10-20 discretization points are enough for the particle. The ordinary differential equations (ODEs) created are solved with respect to time together with the ODEs of the bulk phase. Since the system is stiff, the computer code of Hindmarsh [51] is used as the ODE solver. In general, the simulations progressed without numerical problems. The final values of the rate constants, along with their temperature dependencies, can be obtained with nonlinear regression analysis. The differential equations were solved in situ with the backward... 

Control of nutrient transport dictates significant coupling between transported components in G1 epithelia. This complicates solute transport analysis by requiring a multicomponent description. Flux equations written for each component constitute a nonlinear system in which the coupling nonlinearities are embodied in the coefficients modifying individual transport contributions to flux. 

The second considered example is described by the monostable potential of the fourth order (x) = ax4/4. In this nonlinear case the applicability of exponential approximation significantly depends on the location of initial distribution and the noise intensity. Nevertheless, the exponential approximation of time evolution of the mean gives qualitatively correct results and may be used as first estimation in wide range of noise intensity (see Fig. 14, a = 1). Moreover, if we will increase noise intensity further, we will see that the error of our approximation decreases and for kT = 50 we obtain that the exponential approximation and the results of computer simulation coincide (see Fig. 15, plotted in the logarithmic scale, a = 1, xo = 3). From this plot we can conclude that the nonlinear system is linearized by a strong noise, an effect which is qualitatively obvious but which should be investigated further by the analysis of variance and higher cumulants. 

Although the importance of a systemic perspective on metabolism has only recently attained widespread attention, a formal frameworks for systemic analysis has already been developed since the late 1960s. Biochemical Systems Theory (BST), put forward by Savageau and others [142, 144 147], seeks to provide a unified framework for the analysis of cellular reaction networks. Predating Metabolic Control Analysis, BST emphasizes three main aspects in the analysis of metabolism [319] (i) the importance of the interconnections, rather than the components, for cellular function (ii) the nonlinearity of biochemical rate equations (iii) the need for a unified mathematical treatment. Similar to MCA, the achievements associated with BST would warrant a more elaborate treatment, here we will focus on BST solely as a tool for the approximation and numerical simulation of complex biochemical reaction networks. 

Steady-state process simulation or process flowsheeting has become a routine activity for process analysis and design. Such systems allow the development of comprehensive, detailed, and complex process models with relatively little effort. Embedded within these simulators are rigorous unit operations models often derived from first principles, extensive physical property models for the accurate description of a wide variety of chemical systems, and powerful algorithms for the solution of large, nonlinear systems of equations. 

Competitive, 249, 123, 146, 190 [partial, 249, 124 progress curve equations for, 249, 176, 180 for three-substrate systems, 249, 133, 136] competitive-uncompetitive, 249, 138 concave-up hyperbolic, 249, 143 dead-end, 249, 124 [for bireactant kinetic mechanism determination, 249, 130-133 definition of kinetic constants, 249, 220-221 effects on enzyme progress curves, nonlinear regression analysis, 249, 71-72 inhibition constant evaluation, 249, 134-135 kinetic analysis with, 249, 123-143 one-substrate systems, 249, 124-126 unireactant systems, theory,... 

After an overview of the main papers devoted to chaos in lasers (Section I.A) and in nonlinear optical processes (Section I.B), we present a more detailed analysis of dynamics in a process of second-harmonic generation of light (Section II) as well as in Kerr oscillators (Section III). The last case we consider particularly in the context of coupled nonlinear systems. Finally, we present a cumulant approach to the problem of quantum corrections to the classical dynamics in second-harmonic generation and Kerr processes (Section IV). 

P 21.4 Stability Analysis of Steady-States of Nonlinear Systems Analyze the stability of the steady-state C, = 0 of Figs. 21.6a and b. 

The analysis of polymer processing is reduced to the balance equations, mass or continuity, energy, momentum and species and to some constitutive equations such as viscosity models, thermal conductivity models, etc. Our main interest is to solve this coupled nonlinear system of equations as accurately as possible with the least amount of computational effort. In order to do this, we simplify the geometry, we apply boundary and initial conditions, we make some physical simplifications and finally we chose an appropriate constitutive equations for the problem. At the end, we will arrive at a mathematical formulation for the problem represented by a certain function, say / (x, T, p, u,...), valid for a domain V. Due to the fact that it is impossible to obtain an exact solution over the entire domain, we must introduce discretization, for example, a grid. The grid is just a domain partition, such as points for finite difference methods, or elements for finite elements. Independent of whether the domain is divided into elements or points, the solution of the problem is always reduced to a discreet solution of the problem variables at the points or nodal pointsinxxnodes. The choice of grid, i.e., type of element, number of points or nodes, directly affects the solution of the problem. 

However, as discussed in the introduction, living systems can exhibit much more complicated temporal behaviors. The purpose of the present section is to illustrate how different forms of complex dynamics can arise in the electrophysiological activity of pancreatic f) -cells, and to show how these phenomena can be accounted for in a simple nonlinear dynamic analysis. 

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