Fractional Order Filter Discretization With Marine Predators Algorithm

2021 ◽  
Author(s):  
Abdullah Ates ◽  
YangQuan Chen
Keyword(s):  
Uniform Distribution ◽  
Fractional Order ◽  
Discrete Time ◽  
Fourth Order ◽  
Continuous Time ◽  
Heuristic Method ◽  
Hunting Behavior ◽  
Time Frequency ◽  
Frequency Responses ◽  
Marine Predators

Abstract In this study, discrete time models of continuous time fractional order filters are obtained by using the Marine Predators Algorithm (MPA). Marine Predators optimization algorithm is a population-based heuristic method. This method is inspired by the hunting behavior of marine predators. The algorithm works on three basic phases. These phases occur according to the difference or equality of the velocity of the prey and the predator. As it is known, uniform distribution is generally used in stochastic based optimization algorithms. However, in the MPA method, Brownian and Levy distributions are also used as well as uniform distribution. First, continuous time frequency responses of fractional order filters are generated. Then, fourth order discrete time filters are designed that can give similar responses with generated continues time filter frequency responses. Ten parameters were optimized for the design of fourth order discrete time filters numerator and denominator. The Marine Predators method’s results are compared with the results of the Fractional order Darwinian Particle Swarm Optimization (FODPSO) algorithm, from which discrete time filters are obtained for two fractional order continuous time filter models. In this way, it has been shown comparatively that the Marine Predators Algorithm can be used in real engineering problems and can do filter discretization better.

scholarly journals A Novel Random Number Generator Based on Fractional Order Chaotic Chua System

2020 ◽  
Vol 26 (1) ◽  
pp. 52-57 ◽  
Author(s):  
Fatih Ozkaynak
Keyword(s):  
Fractional Order ◽  
Discrete Time ◽  
Continuous Time ◽  
Chaotic Systems ◽  
Random Number ◽  
Random Number Generator ◽  
Number Generator ◽  
Practical Applications ◽  
The One ◽  
Time Systems

One of the practical applications of chaotic systems is the design of a random number generator. In the literature, generally random number generators are designed using discrete time chaotic systems. The reason for the use of the discrete time chaotic systems in the design architecture is that the latter have a simpler structure than the continuous time chaotic systems. In order to observe chaos in continuous time systems, the system must have at least three degrees. It is shown that for fractional order chaotic systems chaos can be observed even in a lower system degree. The aim of this study is to develop a random number generator using a fractional order chaotic Chua system. The proposed generator is analysed using various randomness tests. The analysis results show that the proposed generator passes the random requirements successfully. On the one hand, this study is important because it demonstrates the practical application of fractional order chaotic systems. On the other hand, it provides an alternative to designs based on discrete time chaotic systems.

scholarly journals Puu System of Fractional Order and Its Chaos Suppression

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 340 ◽  
Author(s):  
Marius-F. Danca
Keyword(s):  
Periodic Solutions ◽  
Fractional Order ◽  
Discrete Time ◽  
Continuous Time ◽  
Control Algorithm ◽  
Chaos Control ◽  
Chaos Suppression ◽  
Discrete Time Systems ◽  
Time Systems ◽  
Order Continuous

In this paper, the fractional-order variant of Puu’s system is introduced, and, comparatively with its integer-order counterpart, some of its characteristics are presented. Next, an impulsive chaos control algorithm is applied to suppress the chaos. Because fractional-order continuous-time or discrete-time systems have not had non-constant periodic solutions, chaos suppression is considered under some numerical assumptions.

Design of Programmable Wideband Low Pass Filter with Continuous-Time/Discrete-Time Hybrid Architecture

2017 ◽  
Vol E100.C (10) ◽  
pp. 858-865 ◽  
Author(s):  
Yohei MORISHITA ◽  
Koichi MIZUNO ◽  
Junji SATO ◽  
Koji TAKINAMI ◽  
Kazuaki TAKAHASHI
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Hybrid Architecture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Low Pass

CONTINUOUS-TIME FRACTIONAL ORDER LINEAR SYSTEMS IDENTIFICATION USING CHEBYSHEV WAVELET

2020 ◽  
Vol 6 (8(77)) ◽  
pp. 23-28
Author(s):  
Shuen Wang ◽  
Ying Wang ◽  
Yinggan Tang
Keyword(s):  
Linear Systems ◽  
Fractional Order ◽  
Continuous Time ◽  
Fractional Integration ◽  
Operational Matrices ◽  
Computation Complexity ◽  
Order Linear ◽  
Fractional Integration Operator ◽  
Systems Identification ◽  
Chebyshev Wavelet

In this paper, the identification of continuous-time fractional order linear systems (FOLS) is investigated. In order to identify the differentiation or- ders as well as parameters and reduce the computation complexity, a novel identification method based on Chebyshev wavelet is proposed. Firstly, the Chebyshev wavelet operational matrices for fractional integration operator is derived. Then, the FOLS is converted to an algebraic equation by using the the Chebyshev wavelet operational matrices. Finally, the parameters and differentiation orders are estimated by minimizing the error between the output of real system and that of identified systems. Experimental results show the effectiveness of the proposed method.

scholarly journals Time to Intervene: A Continuous-Time Approach to Network Analysis and Centrality

Psychometrika ◽  
2021 ◽  
Author(s):  
Oisín Ryan ◽  
Ellen L. Hamaker
Keyword(s):  
Network Analysis ◽  
Discrete Time ◽  
Continuous Time ◽  
Centrality Measures ◽  
Time Interval ◽  
Direct Effects ◽  
Network Approach ◽  
Var Models ◽  
Auto Regressive ◽  
Conceptual Shift

AbstractNetwork analysis of ESM data has become popular in clinical psychology. In this approach, discrete-time (DT) vector auto-regressive (VAR) models define the network structure with centrality measures used to identify intervention targets. However, VAR models suffer from time-interval dependency. Continuous-time (CT) models have been suggested as an alternative but require a conceptual shift, implying that DT-VAR parameters reflect total rather than direct effects. In this paper, we propose and illustrate a CT network approach using CT-VAR models. We define a new network representation and develop centrality measures which inform intervention targeting. This methodology is illustrated with an ESM dataset.

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