A Statistical Study of Generalized Cell Mapping

1988 ◽  
Vol 55 (3) ◽  
pp. 694-701 ◽  
Author(s):  
Jian-Qiao Sun ◽  
C. S. Hsu
Keyword(s):  
Stochastic Systems ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Mapping Method ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
One Step ◽  
The Mean ◽  
Step Transition ◽  
The One

In this paper a statistical error analysis of the generalized cell mapping method for both deterministic and stochastic dynamical systems is examined, based upon the statistical analogy of the generalized cell mapping method to the density estimation. The convergence of the mean square error of the one step transition probability matrix of generalized cell mapping for deterministic and stochastic systems is studied. For stochastic systems, a well-known trade-off feature of the density estimation exists in the mean square error of the one step transition probability matrix, which leads to an optimal design of generalized cell mapping for stochastic systems. The conclusions of the study are illustrated with some examples.

On the Data-Driven Generalized Cell Mapping Method

2019 ◽  
Vol 29 (14) ◽  
pp. 1950204 ◽  
Author(s):  
Zigang Li ◽  
Jun Jiang ◽  
Ling Hong ◽  
Jian-Qiao Sun
Keyword(s):  
Global Analysis ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Mapping Method ◽  
Data Driven ◽  
Cell Mapping ◽  
Time Step ◽  
Generalized Cell Mapping ◽  
Step Transition ◽  
The One

Global analysis is often necessary for exploiting various applications or understanding the mechanisms of many dynamical phenomena in engineering practice where the underlying system model is too complex to analyze or even unavailable. Without a mathematical model, however, it is very difficult to apply cell mapping for global analysis. This paper for the first time proposes a data-driven generalized cell mapping to investigate the global properties of nonlinear systems from a sequence of measurement data, without prior knowledge of the underlying system. The proposed method includes the estimation of the state dimension of the system and time step for creating a mapping from the data. With the knowledge of the estimated state dimension and proper mapping time step, the one-step transition probability matrix can be computed from a statistical approach. The global properties of the underlying system can be uncovered with the one-step transition probability matrix. Three examples from applications are presented to illustrate a quality global analysis with the proposed data-driven generalized cell mapping method.

A Nonuniform Cell Partition for the Analysis of Nonlinear Stochastic Systems

1998 ◽  
Vol 65 (4) ◽  
pp. 867-869 ◽  
Author(s):  
J. Q. Sun
Keyword(s):  
Ad Hoc ◽  
Stochastic Systems ◽  
Transition Probability ◽  
Nonlinear Stochastic Systems ◽  
Transition Probability Density ◽  
Generalized Cell Mapping ◽  
One Step ◽  
Step Transition ◽  
The One ◽  
Cell Partition

This paper presents a study of nonuniform cell partition for analyzing the response of nonlinear stochastic systems by using the generalized cell mapping (GCM) method. The necessity of nonuniform cell partition for nonlinear systems is discussed first. An ad hoc scheme is then presented for determining optimal cell sizes based on the statistical analysis of the GCM method. The proposed nonuniform cell partition provides a roughly uniform accuracy for the estimate of the one-step transition probability density function over a large region in the state space where the system varies significantly from being linear to being strongly nonlinear. The nonuniform cell partition is shown to lead to more accurate steady-state solutions and enhance the computational efficiency of the GCM method.

The Generalized Cell Mapping Method in Nonlinear Random Vibration Based Upon Short-Time Gaussian Approximation

1990 ◽  
Vol 57 (4) ◽  
pp. 1018-1025 ◽  
Author(s):  
J. Q. Sun ◽  
C. S. Hsu
Keyword(s):  
Steady State ◽  
Gaussian Approximation ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Generalized Cell Mapping ◽  
Mean Square Response ◽  
One Step ◽  
Step Transition ◽  
The One ◽  
Short Time

A short-time Gaussian approximation scheme is proposed in the paper. This scheme provides a very efficient and accurate way of computing the one-step transition probability matrix of the previously developed generalized cell mapping (GCM) method in nonlinear random vibration. The GCM method based upon this scheme is applied to some very challenging nonlinear systems under external and parametric Gaussian white noise excitations in order to show its power and efficiency. Certain transient and steady-state solutions such as the first-passage time probability, steady-state mean square response, and the steady-state probability density function have been obtained. Some of the solutions are compared with either the simulation results or the available exact solutions, and are found to be very accurate. The computed steady-state mean square response values are found to be of error less than 1 percent when compared with the available exact solutions. The efficiency of the GCM method based upon the short-time Gaussian approximation is also examined. The short-time Gaussian approximation renders the overhead of computing the one-step transition probability matrix to be very small. It is found that in a comprehensive study of nonlinear stochastic systems, in which various transient and steady-state solutions are obtained in one computer program execution, the GCM method can have very large computational advantages over Monte Carlo simulation.

The Reliability Analysis of Mechanical Leg (2UPS+UP) Based on a Discrete Time Repairable Markov Model

2015 ◽  
Vol 713-715 ◽  
pp. 760-763
Author(s):  
Jia Lei Zhang ◽  
Zhen Lin Jin ◽  
Dong Mei Zhao
Keyword(s):  
Steady State ◽  
Discrete Time ◽  
Transition Probability ◽  
Continuous Model ◽  
Transition Probability Matrix ◽  
State Equations ◽  
One Step ◽  
The Difference ◽  
Step Transition ◽  
The One

We have analyzed some reliability problems of the 2UPS+UP mechanism using continuous Markov repairable model in our previous work. According to the check and repair of the robot is periodic, the discrete time Markov repairable model should be more appropriate. Firstly we built up the discrete time repairable model and got the one step transition probability matrix. Secondly solved the steady state equations and got the steady state availability of the mechanical leg, by the solution of the difference equations the reliability and the mean time to first failure were obtained. In the end we compared the reliability indexes with the continuous model.

The Stochastic Response of a Class of Impact Systems Calculated by a New Strategy Based on Generalized Cell Mapping Method

2018 ◽  
Vol 85 (5) ◽  
Author(s):  
Liang Wang ◽  
Shichao Ma ◽  
Chunyan Sun ◽  
Wantao Jia ◽  
Wei Xu
Keyword(s):  
Transition Probability ◽  
Transition Probability Matrix ◽  
Mapping Method ◽  
Cell Mapping ◽  
Stochastic Response ◽  
Generalized Cell Mapping ◽  
Significant Difference ◽  
New Strategy ◽  
Almost All ◽  
The Impact

In this paper, a new strategy based on generalized cell mapping (GCM) method will be introduced to investigate the stochastic response of a class of impact systems. Significant difference of the proposed procedure lies in the choice of a novel impact-to-impact mapping, which is built to calculate the one-step transition probability matrix, and then, the probability density functions (PDFs) of the stochastic response can be obtained. The present strategy retains the characteristics of the impact systems, and is applicable to almost all types of impact systems indiscriminately. Further discussion proves that our strategy is reliable for different white noise excitations. Numerical simulations verify the efficiency and accuracy of the suggested strategy.

Studying the Global Bifurcation Involving Wada Boundary Metamorphosis by a Method of Generalized Cell Mapping with Sampling-Adaptive Interpolation

2018 ◽  
Vol 28 (02) ◽  
pp. 1830003 ◽  
Author(s):  
Xiao-Ming Liu ◽  
Jun Jiang ◽  
Ling Hong ◽  
Dafeng Tang
Keyword(s):  
Global Bifurcation ◽  
Mapping Method ◽  
Computational Time ◽  
Cell Mapping ◽  
Third Order ◽  
Pendulum System ◽  
Generalized Cell Mapping ◽  
Adaptive Interpolation ◽  
One Step ◽  
Probability Transition Matrix

In this paper, a new method of Generalized Cell Mapping with Sampling-Adaptive Interpolation (GCMSAI) is presented in order to enhance the efficiency of the computation of one-step probability transition matrix of the Generalized Cell Mapping method (GCM). Integrations with one mapping step are replaced by sampling-adaptive interpolations of third order. An explicit formula of interpolation error is derived for a sampling-adaptive control to switch on integrations for the accuracy of computations with GCMSAI. By applying the proposed method to a two-dimensional forced damped pendulum system, global bifurcations are investigated with observations of boundary metamorphoses including full to partial and partial to partial as well as the birth of fully Wada boundary. Moreover GCMSAI requires a computational time of one thirtieth up to one fiftieth compared to that of the previous GCM.

A Cell Mapping Method for Nonlinear Deterministic and Stochastic Systems—Part II: Examples of Application

1986 ◽  
Vol 53 (3) ◽  
pp. 702-710 ◽  
Author(s):  
H. M. Chiu ◽  
C. S. Hsu
Keyword(s):  
Present Method ◽  
Stochastic Systems ◽  
Random Excitation ◽  
Mapping Method ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Deterministic Systems ◽  
Strongly Nonlinear ◽  
Stochastic Problems ◽  
A Cell

In this second part of the two-part paper we demonstrate the viability of the compatible simple and generalized cell mapping method by applying it to various deterministic and stochastic problems. First we consider deterministic problems with non-chaotic responses. For this class of problems we show how system responses and domains of attraction can be obtained by a refining procedure of the present method. Then, we consider stochastic problems with stochasticity lying in system parameters or excitation. Next, deterministic systems with chaotic responses are considered. By the present method, finding the statistical responses of such systems under random excitation also presents no difficulties. Some of the systems studied here are well-known. New results are, however, also obtained. These are results on Duffing systems with a stochastic coefficient, the global results of a Duffing system shown in Section 4, the results on strongly nonlinear Duffing systems under random excitations reported in Section 7.2, and the strange attractor results for systems subjected to random excitations.

Stabilité de la récurrence nulle pour certaines chaines de Markov perturbées

1983 ◽  
Vol 20 (3) ◽  
pp. 482-504 ◽  
Author(s):  
C. Cocozza-Thivent ◽  
C. Kipnis ◽  
M. Roussignol
Keyword(s):  
Markov Chain ◽  
Markov Chains ◽  
Transition Probability ◽  
The Other ◽  
Barrier Functions ◽  
One Step ◽  
Null Recurrence ◽  
Step Transition ◽  
The One ◽  
Taylor's Formula

We investigate how the property of null-recurrence is preserved for Markov chains under a perturbation of the transition probability. After recalling some useful criteria in terms of the one-step transition nucleus we present two methods to determine barrier functions, one in terms of taboo potentials for the unperturbed Markov chain, and the other based on Taylor's formula.

Fuzzy Logic and Cell to Cell Mapping

1996 ◽  
Author(s):  
D. Edwards ◽  
H. T. Choi ◽  
J. Canning
Keyword(s):  
Fuzzy Logic ◽  
Nonlinear System ◽  
Nonlinear Systems ◽  
Chaotic System ◽  
Transition Probability ◽  
Transition Probability Matrix ◽  
Cell Mapping ◽  
Generalized Cell Mapping ◽  
Simple Cell Mapping

Abstract Nonlinear systems are important in many fields of science, mathematics, and engineering. In recent years, simple cell mapping (SCM) and generalized cell mapping (GCM) methods have been proposed and successfully used to analyze nonlinear systems. The GCM method requires the determination of a transition probability matrix. In a manner similar to GCM, we use fuzzy logic to calculate a transition possibility matrix for a nonlinear system. This matrix can then be used to establish the statistical properties of strange attractors associated with a chaotic system. We analyze a chaotic system using fuzzy logic to demonstrate this approach and then compare our result with the GCM method.

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