1992Thresholding of Digital Images Using Two-dimensional Entropies
EVOLUTION OF THE STATE DENSITIES AND THE ENTROPIES OF DYNAMICAL SYSTEMS
-
Upload
querida-carvalho -
Category
Documents
-
view
14 -
download
1
description
Transcript of EVOLUTION OF THE STATE DENSITIES AND THE ENTROPIES OF DYNAMICAL SYSTEMS
This research has been supported in part by European Commission FP6 IYTE-Wireless
Project (Contract No: 017442)
EVOLUTION OF THE STATE DENSITIES AND THE ENTROPIES OF DYNAMICAL SYSTEMS
EVOLUTION OF THE STATE DENSITIES AND THE ENTROPIES OF DYNAMICAL SYSTEMS
Ferit Acar SAVACIIzmir Institute of Technology
Dept. of Electrical Electronics Engineering
Urla 35430, Izmir
Serkan GÜNELDokuz Eylül University
Dept. of Electrical Electronics Engineering
Buca, 35160, Izmir
Contents Deterministic and indeterministic systems under influence of uncertainty...
Evolution of state probability densities Transformations on probability densities
Markov Operators & Frobenius—Perron Operators
Estimating state probability densities using kernel density estimators Parzen’s density estimator Density estimates for Logistic Map and Chua’s Circuit
The 2nd Law of Thermodynamics and Entropy Estimating Entropy of the system using kernel density estimations
Entropy Estimates for Logistic Map and Chua’s Circuit Entropy in terms of Frobenius—Perron Operators
Entropy and Control Maximum Entropy Principle Effects of external disturbance and observation on the system entropy Controller as a entropy changing device Equivalence of Maximum Entropy minimization to Optimal Control
Motivation
Thermal noise effects all dynamical systems,
Exciting the systems by noise can alter the dynamics radically causing interesting behavior such as stochastic resonances,
Problems in chaos control with bifurcation parameter perturbations,
Possibility of designing noise immune control systems Densities arise whenever there is uncertainty in system
parameters, initial conditions etc. even if the systems under study are deterministic.
Frobenius—Perron Operators
Definition
Evolution of The State Densities of The Stochastic Dynamical Systems
• i’s are 1D Wiener Processes
Fokker—Planck—Kolmogorov Equ.
• p0(x) : Initial probability density of the states
Infinitesimal Operator of Frobenius—Perron Operator
AFP : D(X)D(X)
D(X): Space of state probability densities
FPK equation in noiseless case
Stationary Solutions of FPK Eq.
Reduced Fokker—Planck—Kolmogorov Equ.
Frobenius—Perron Operator
x0
x1 xn-1
xnS
S(n-2)S
X
D(X)
f0
f1 fn-1
fn
n-2
Calcutating FPO
S differentiable & invertible
Logistic Map
α=4
Estimating Densities from Observed Data Parzen’s Estimator
Observation vector : d i=1,...,n
} = 1
Logistic Map — Parzen’s Estimation
Logistic Map =4
Chua’s Circuit
-E
E
Chua’s Circuit — Dynamics
Chua’s Circuit — The state densities
x
p(x)
Limit Cycles
Period-2 Cycles
Scrolls
Double Scroll
Details
The 2nd Law of Thermodynamics & Information
0
T
QH
Q : Energy transfered to the systemT : Temprature (Average Kinetic Energy)
Classius Boltzman
Thermodynamics
Shannon
n: number of events pi: probability of event “i”
Information Theory
Entropy = Disorder of the system = Information gained by observing the systemEntropy = Disorder of the system = Information gained by observing the system
Entropy
Estimated Entropy – Logistic Map
Estimated Entropy — Chua’s Circuit
Estimated Entropy — Chua’s Circuit II
Entropy in Control Systems I External Effects
x(t)p(x)
e(t)p(e)
If State transition transformation is measure preserving, then
Change in entropy :
Observer Entropy
x(t)p(x)
y(t)p(y)
Entropy of Control Systems II Mutual Information
Theorem
Uncertain v.s. Certain Controller Theorem
Theorem
Principle of Maximum Entropy
Theorem
Optimal Control with Uncertain Controller II
Select p(u) to maximize
subject to
Optimal Control with Uncertain Controller III
Optimal Control with Uncertain Controller IV
Optimal Control with Uncertain Controller V
Theorem
Summary I The state densities of nonlinear dynamical systems can be estimated
using kernel density estimators using the observed data which can be used to determine the evolution of the entropy.
Important observation : Topologically more complex the dynamics results in higher stationary entropy
The evolution of uncertainty is a trackable problem in terms of Fokker—Planck—Kolmogorov formalism.
The dynamics in the state space are converted to an infinite dimensional system given by a linear parabolic partial diff. equation (The FPK Equation),
The solution of the FPK can be reduced to finding solution of a set of nonlinear algebraic equations by means of weighted residual schemes,
The worst case entropy can be used as a performance criteria to be minimized(maximized) in order to force the system to a topologically simpler dynamics.
Summary II
The (possibly stochastic) controller performance is determined by the information gather by the controller about the actual system state.
A controller that reduces the entropy of a dynamical system must increase its entropy at least by the reduction to be achieved.