scholarly journals A new approach to fuzzy sets: Application to the design of nonlinear time series, symmetry-breaking patterns, and non-sinusoidal limit-cycle oscillations

2019 ◽  
Vol 128 ◽  
pp. 191-202 ◽  
Author(s):  
Vladimir García-Morales
2009 ◽  
Vol 19 (02) ◽  
pp. 453-485 ◽  
Author(s):  
MINGHAO YANG ◽  
ZHIQIANG LIU ◽  
LI LI ◽  
YULIN XU ◽  
HONGJV LIU ◽  
...  

Some chaotic and a series of stochastic neural firings are multimodal. Stochastic multimodal firing patterns are of special importance because they indicate a possible utility of noise. A number of previous studies confused the dynamics of chaotic and stochastic multimodal firing patterns. The confusion resulted partly from inappropriate interpretations of estimations of nonlinear time series measures. With deliberately chosen examples the present paper introduces strategies and methods of identification of stochastic firing patterns from chaotic ones. Aided by theoretical simulation we show that the stochastic multimodal firing patterns result from the effects of noise on neuronal systems near to a bifurcation between two simpler attractors, such as a point attractor and a limit cycle attractor or two limit cycle attractors. In contrast, the multimodal chaotic firing trains are generated by the dynamics of a specific strange attractor. Three systems were carefully chosen to elucidate these two mechanisms. An experimental neural pacemaker model and the Chay mathematical model were used to show the stochastic dynamics, while the deterministic Wang model was used to show the deterministic dynamics. The usage and interpretation of nonlinear time series measures were systematically tested by applying them to firing trains generated by the three systems. We successfully identified the distinct differences between stochastic and chaotic multimodal firing patterns and showed the dynamics underlying two categories of stochastic firing patterns. The first category results from the effects of noise on the neuronal system near a Hopf bifurcation. The second category results from the effects of noise on the period-adding bifurcation between two limit cycles. Although direct application of nonlinear measures to interspike interval series of these firing trains misleadingly implies chaotic properties, definition of eigen events based on more appropriate judgments of the underlying dynamics leads to accurate identifications of the stochastic properties.


Author(s):  
JL Casado Corpas ◽  
A Sanz-Lobera ◽  
I González-Requena ◽  
L Sevilla

Computational fluid dynamic and order reducing methods have been extensively applied to predict the flutter onset speed of several types of aircrafts. However, the accuracy required by certification standards still ascribes flight testing as the only method available that safely validates the flight envelope of an aircraft. In particular, free-flutter conditions must be demonstrated in the target flight envelope, and several methods have been developed to determine the flutter onset speed in real-time when expanding the envelope during flight testing. Among the methods, the damping versus velocity technique combined with a flutter margin implementation remains the most common technique used for envelope expansion. Even with the popularity and “easy to implement” characteristics of this method, several shortcomings can adversely affect the identification of non-stable conditions during envelope expansion. Notably, the limit cycle oscillations conditions, distinct from flutter, cannot be accurately identified. This study proposes to apply a similar methodology to the flutter margin to anticipate limit cycle oscillations associated with freeplay in the plunge axis of a bi-dimensional airfoil that is aeroelastically representative of the tested aircraft. Analytical considerations are conducted to support this new approach, and a computer model is used to validate the proposed methodology.


2011 ◽  
Vol 3 (2) ◽  
pp. 11-15
Author(s):  
Seng Hansun

Recently, there are so many soft computing methods been used in time series analysis. One of these methods is fuzzy logic system. In this paper, we will try to implement fuzzy logic system to predict a non-stationary time series data. The data we use here is Mackey-Glass chaotic time series. We also use MATLAB software to predict the time series data, which have been divided into four groups of input-output pairs. These groups then will be used as the input variables of the fuzzy logic system. There are two scenarios been used in this paper, first is by using seven fuzzy sets, and second is by using fifteen fuzzy sets. The result shows that the fuzzy system with fifteen fuzzy sets give a better forecasting result than the fuzzy system with seven fuzzy sets. Index Terms—forecasting, fuzzy logic, Mackey-Glass chaotic, MATLAB, time series analysis


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