scholarly journals Outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Weike Cheng ◽  
Ailong Wu ◽  
Jin-E Zhang ◽  
Biwen Li
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Control Strategies ◽  
High Reliability ◽  
Low Cost ◽  
Data Sampling ◽  
Fractional Systems ◽  
Outer Synchronization ◽  
Deviating Argument ◽  
Synchronization Theory

Abstract This paper is committed to investigating outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches. Considering the low cost and high reliability of data-sampling control, we adopt two categories of control strategies with principles of centralized and decentralized data-sampling to synchronize fractional-order neural networks with deviating argument. Several sufficient criteria are proposed to realize outer-synchronization by data-sampling control design in two complex coupled networks. It is noteworthy that, based on centralized and decentralized data-sampling methods, the synchronization theory of fractional systems and differential equation with deviating argument, the sampling time points are very well selected in control systems. An example is performed to illustrate the advantage of the presented theoretical analysis and results.

scholarly journals Centralized and Decentralized Data-Sampling Principles for Outer-Synchronization of Fractional-Order Neural Networks

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Jin-E Zhang
Keyword(s):  
Neural Networks ◽  
Differential Equations ◽  
Fractional Order ◽  
Fractional Differential Equations ◽  
Data Sampling ◽  
Outer Synchronization ◽  
Numerical Example ◽  
State Dependent ◽  
Fractional Differential ◽  
Theoretical Results

This paper aims to investigate the outer-synchronization of fractional-order neural networks. Using centralized and decentralized data-sampling principles and the theory of fractional differential equations, sufficient criteria about outer-synchronization of the controlled fractional-order neural networks are derived for structure-dependent centralized data-sampling, state-dependent centralized data-sampling, and state-dependent decentralized data-sampling, respectively. A numerical example is also given to illustrate the superiority of theoretical results.

scholarly journals Global Mittag–Leffler Stabilization of Fractional-Order BAM Neural Networks with Linear State Feedback Controllers

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hongyun Yan ◽  
Yuanhua Qiao ◽  
Lijuan Duan ◽  
Ling Zhang
Keyword(s):  
Neural Networks ◽  
Lyapunov Function ◽  
Fractional Order ◽  
State Feedback ◽  
Control Strategies ◽  
Sufficient Conditions ◽  
State Feedback Control ◽  
Bam Neural Networks ◽  
Linear State ◽  
The Stability

In this paper, the global Mittag–Leffler stabilization of fractional-order BAM neural networks is investigated. First, a new lemma is proposed by using basic inequality to broaden the selection of Lyapunov function. Second, linear state feedback control strategies are designed to induce the stability of fractional-order BAM neural networks. Third, based on constructed Lyapunov function, generalized Gronwall-like inequality, and control strategies, several sufficient conditions for the global Mittag–Leffler stabilization of fractional-order BAM neural networks are established. Finally, a numerical simulation is given to demonstrate the effectiveness of our theoretical results.

Adaptive projective synchronization for time-delayed fractional-order neural networks with uncertain parameters and its application in secure communications

2017 ◽  
Vol 40 (10) ◽  
pp. 3078-3087 ◽  
Author(s):  
Xiaona Song ◽  
Shuai Song ◽  
Bo Li ◽  
Ines Tejado Balsera
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Hybrid Control ◽  
Control Strategies ◽  
Lyapunov Stability Theory ◽  
Projective Synchronization ◽  
Linear Matrix ◽  
Secure Communications ◽  
Uncertain Parameters ◽  
Matrix Inequalities

In this paper, the adaptive projective synchronization of time-delayed fractional-order neural networks is considered. Using the active control and adaptive control methods, efficient hybrid control strategies are designed for time-delayed fractional-order neural networks with uncertain parameters. Based on a new version of fractional-order Lyapunov stability theory, the projective synchronization conditions are addressed in terms of linear matrix inequalities, which is easily checked and applied to practical systems. Finally, numerical simulations and application of the proposed methods to secure communications have been presented to validate the synchronization method.

scholarly journals Centralized Data-Sampling Approach for GlobalOt-αSynchronization of Fractional-Order Neural Networks with Time Delays

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Jin-E Zhang
Keyword(s):  
Neural Networks ◽  
Fractional Order ◽  
Time Delays ◽  
Leibniz Rule ◽  
Data Sampling ◽  
Control Performance ◽  
Sufficient Criterion ◽  
Coupled Neural Networks ◽  
Synchronization Problem ◽  
Sampling Approach

In this paper, the globalO(t-α)synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize theO(t-α)synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve globalO(t-α)synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme.

Synchronization for Incommensurate Riemann–Liouville Fractional-Order Time-Delayed Competitive Neural Networks With Different Time Scales and Known or Unknown Parameters1

2019 ◽  
Vol 14 (5) ◽  
Author(s):  
Yajuan Gu ◽  
Hu Wang ◽  
Yongguang Yu
Keyword(s):  
Neural Networks ◽  
Time Scales ◽  
Fractional Order ◽  
Secure Communication ◽  
Time Delays ◽  
Chaos Synchronization ◽  
Unknown Parameters ◽  
Fractional Systems ◽  
Master System ◽  
Different Time Scales

Synchronization for incommensurate Riemann–Liouville fractional competitive neural networks (CNN) with different time scales is investigated in this paper. Time delays and unknown parameters are concerned in the model, which is more practical. Two simple and effective controllers are proposed, respectively, such that synchronization between the salve system and the master system with known or unknown parameters can be achieved. The methods are more general and less conservative which can also be applied to commensurate integer-order systems and commensurate fractional systems. Furthermore, two numerical ensamples are provided to show the feasibility of the approach. Based on the chaotic masking method, the example of chaos synchronization application for secure communication is provided.

Modelling of bacterial removal in wastewater storage reservoir for irrigation purposes: a case study in Sicily, Italy

2003 ◽  
Vol 3 (4) ◽  
pp. 169-175 ◽  
Author(s):  
S. Barbagallo ◽  
F. Brissaud ◽  
G.L. Cirelli ◽  
S. Consoli ◽  
P. Xu
Keyword(s):  
Municipal Wastewater ◽  
High Reliability ◽  
Low Cost ◽  
Public Awareness ◽  
Storage System ◽  
Wastewater Reuse ◽  
Order Kinetic ◽  
Aquifer Recharge ◽  
Mediterranean Countries ◽  
Storage Reservoirs

In arid and semiarid regions the reclamation and reuse of municipal wastewater can play a strategic role in alleviating water resources shortages. Public awareness is growing about the need to recycle and reuse water for increasing supply availability. Many wastewater reuse projects have been put in operation in European and Mediterranean countries adopting extensive treatment systems such as aquifer recharge, lagooning, constructed wetlands, and storage reservoirs, mainly for landscape and agricultural irrigation. In agricultural reuse systems, there is an increasing interest in extensive technologies because of their high reliability, and easy and low cost operation and maintenance. Wastewater storage reservoirs have become the option selected in many countries because of the advantages they present in comparison with other treatment alternatives, namely the coupling of two purposes, stabilization and seasonal regulation. This paper describes an example of a wastewater storage system, built in Caltagirone (Sicily, Italy). The storage results in a tertiary treatment of a continuous inlet flow of activated sludge effluents. The prediction of the microbiological water quality has been evaluated by means of a non-steady-state first-order kinetic model. Single and multiple regressions were applied to determine the main variables that most significantly affected die-off coefficients. The proposed model has been calibrated using the results of a field monitoring carried out during a period from March to October 2000.

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