
A nonlinear dynamical system can in general exhibit one or more of the following types of behaviour:
The type of behaviour may depend on the initial state of the system and the values of its parameters, if any.
The most famous type of behaviour is chaotic motion, a nonperiodic complex motion which has given name to the theory. In order to classify the behaviour of a system as chaotic, the system must be bounded and have what is called sensitivity on the initial conditions. This means that two such systems with however small a difference in their initial state eventually will end up with a finite difference between their states.
An example of such sensitivity is the wellknown butterfly effect, whereby the flapping of a butterfly's wings produces tiny changes in the atmosphere which over the course of time cause it to diverge from what it would have been and potentially cause something as dramatic as a tornado to occur. Other commonly known examples of chaotic motion are the mixing of colored dyes and airflow turbulence.
One way of visualizing chaotic motion, or indeed any type of motion, is to make a phase diagram of the motion. In such a diagram time is implicit and each axis represents one dimension of the state. For instance, a system at rest will be plotted as a point and a system in periodic motion will be plotted as a simple closed curve.
A phase diagram for a given system may depend on the initial state of the system (as well as on a set of parameters), but often phase diagrams reveal that the system ends up doing the same motion for all initial states in a region around the motion, almost as though the system is attracted to that motion. Such attractive motion is fittingly called an attractor for the system and is very common for forced dissipative systems.
While most of the motion types mentioned above give rise to very simple attractors, such as points and circlelike curves called limit cycles[?], chaotic motion gives rise to what are known as strange attractors, attractors that can have great detail and complexity. For instance, a simple threedimensional model of the Lorenz weather system gives rise to the famous Lorenz attractor. The Lorenz attractor is perhaps one of the best known chaotic system diagrams, probably because not only was it one of the first, but it is one of the most complex and as such gives rise to a very interesting pattern which looks like the eyes of an owl.
Strange attractors have fractal structure.
The theory has roots back to around 1950 when it first became evident for some scientists that linear theory[?], the prevailing system theory at that time, simply could not explain the observed behaviour of certain experiments like that of the logistic map. However, major parts of the theory have only been developed since around 1980 and only recently has the theory been accepted by the scientific community as a whole.
An early pioneer of the theory was Edward Lorenz whose interest in chaos came about accidentally through his work on weather prediction in 1961. Lorenz was using a basic computer to run his simulation of the weather. He wanted to see a sequence of data again and to save time he started the simulation in the middle of its course. He was able to do this by entering a printout of the data corresponding to conditions in the middle of his simulation which he had calculated last time.
To his surprise the weather that the machine began to predict was completely different to the weather calculated before. Lorenz tracked this down to only bothering to enter 3digit numbers in to the simulation, whereas the computer had last time worked with 5digit numbers. This difference is tiny and the consensus at the time would have been that it should have had practically no effect. However Lorenz had discovered that small changes in initial conditions produced large changes in the longterm outcome.
The importance of chaos theory can be illustrated by the following observations:
Mathematicians have devised many additional ways to make quantitative statements about chaotic systems[?]. These include
Many simple systems can also produce chaos without relying on partial differential equations, such as the logistic equation, which describes population growth over time.
Even discrete systems can heavily depend on initial conditions, such as cellular automata. Stephen Wolfram has investigated a cellular automaton with this property, termed by him rule 30.
Other examples of chaotic systems
See also: fractal, Benoit Mandelbrot, Mandelbrot set, Julia set, predictability, Mitchell Feigenbaum[?]
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