Titlebook: Information Dynamics; Harald Atmanspacher,Herbert Scheingraber Book 1991 Springer Science+Business Media New York 1991 Chaos.algorithm.com

靈敏

A Theory of State Space Reconstruction in the Presence of Noise,ties of the state space reconstruction, such as the sampling time and the reconstruction dimension, and properties of the dynamical system, such as the dimension and Lyapunov exponents. When the dimension and Lyapunov exponents are sufficiently large these scaling laws show that, no matter how the s

scrape

Reconstructing Complexity: Information Dynamics in Acoustic Perception,e is to work out the rationale of this unexpected activeness in terms of performance, and to relate it qualitatively with general principles of information dynamics in a biological context, such as information reduction and the emergence of meaning.

遭受

Glower

Preamble

Non-Boolean Logic of the Theory of Evolutionary Science,re done with a closed set of propositions, otherwise senseless. The non-Boolean figure of logical transformation is the “abduction”. On the basis of a lattice theoretical description of the structure of evolution theory, abduction has a very rigorous (by no means vague or even feuilletonistic) meaning.

類人猿

變形

急急忙忙

Information and Complexity Measures in Dynamical Systems,rding the latter, the possibility of measuring dimension densities and spatial information flow is discussed. In the last part, we investigate the relationship between chaos and (structured) complexity in chaotic dynamical systems, and in particular in systems showing emergent complexity.

Adenoma

Chaotic Data and Model Building,tive function are found to be inferior to the values that the .. estimates suggest are possible. The nature of the models produced by extensive training is found to be sensitive to the choices of initial model parameters.

Employee

Causality and Information Flow,causal information. Since variation in cause produces a correlated variation in effect, the question of which events are causes of which other events is equivalent to the question of how information flows between events. I examine the conditions under which underlying causal structure can be deduced from statistical information.