Finite-time stabilization for fractional-order inertial neural networks with time varying delays

This paper deals with the finite-time stabilization of fractional-order inertial neural network with varying time-delays (FOINNs). Firstly, by correctly selected variable substitution, the system is transformed into a first-order fractional differential equation. Secondly, by building Lyapunov functionalities and using analytical techniques, as well as new control algorithms (which include the delay-dependent and delay-free controller), novel and effective criteria are established to attain the finite-time stabilization of the addressed system. Finally, two examples are used to illustrate the effectiveness and feasibility of the obtained results

Groundwater quality assessment for different uses using various water quality indices in semi-arid region of central Tunisia

The Hajeb Layoun-Jelma basin, located in the central Tunisia, is the principal source of water supply for Sidi Bouzid and Sfax region. The over-abstraction from this groundwater, since 1970, and the intensive agriculture activities led to the degradation of the water quantity and quality. The quality evaluation for this groundwater is very important tool for sustainable development and decision for water management. A total of 28 groundwater samples, from shallow, springs, and deep aquifers, were collected, storage and analyzed to evaluate its quality suitability for domestic and agriculture purposes using geographic information system and geochemical methods. For the both aquifers, the abundance of cations: Na > Mg > Ca > K, and of anions in the order: Cl > HCO3 > SO4. The dominant hydrochemical facies, for the shallow aquifer and springs, are Na-Cl and Ca-Mg-Cl; for the deep aquifer, the geochemical facies are Na-Cl, Ca-Mg-Cl, and Ca-Cl. The comparison of the major parameters and the chemical data with the World Health Organization standards and the national standards indicate that this groundwater is suitable for drinking, except in some samples, with high salinity concentrations. The water quality was assessed, for drinking uses, using "water quality index," "entropy," and "improved water quality index." The results mentioned that the improved water quality index is the best method which indicated that the poor water quality coincide with the Na-Cl water type. The entropy method and the water quality index present the optimistic methods. The irrigation suitability assessment was made using various parameters (SAR, TH, % Na, PI, MH, KR, EC). The results revealed that the majority of samples in Hajeb Layoun-Jelma basin are not appropriate for irrigation uses

Ensemble of heterogeneous flexible neural trees using multiobjective genetic programming

Machine learning algorithms are inherently multiobjective in nature, where approximation error minimization and model's complexity simplification are two conflicting objectives. We proposed a multiobjective genetic programming (MOGP) for creating a heterogeneous flexible neural tree (HFNT), tree-like flexible feedforward neural network model. The functional heterogeneity in neural tree nodes was introduced to capture a better insight of data during learning because each input in a dataset possess different features. MOGP guided an initial HFNT population towards Pareto-optimal solutions, where the final population was used for making an ensemble system. A diversity index measure along with approximation error and complexity was introduced to maintain diversity among the candidates in the population. Hence, the ensemble was created by using accurate, structurally simple, and diverse candidates from MOGP final population. Differential evolution algorithm was applied to fine-tune the underlying parameters of the selected candidates. A comprehensive test over classification, regression, and time-series datasets proved the efficiency of the proposed algorithm over other available prediction methods. Moreover, the heterogeneous creation of HFNT proved to be efficient in making ensemble system from the final population

Stability and global dissipativity for neutral-type fuzzy genetic regulatory networks with mixed delays

In this article, the stability and global dissipativity for neutral-type fuzzy genetic regulatory networks (FGRNs) with mixed time delays are investigated. By using Lyapunov functional method and linear matrix inequalities (LMIs) techniques, new sufficient conditions ensuring the stability and global dissipativity of the considered system are given. Moreover, the globally attractive set and positive invariant set are also presented here. The derived criteria are of the form of LMI and they can be checked by the numerically effect Matlab LMI toolbox. Lastly, two numerical examples with its simulations are proposed to illustrate the effectiveness of the obtained results. The derived results of this article are new and complement many earlier works and the ideas of this work can be applied to investigate other similar systems.Scopu

Global exponential stability of pseudo almost automorphic solutions for delayed Cohen-Grosberg neural networks with measure

summary:We investigate the Cohen-Grosberg differential equations with mixed delays and time-varying coefficient: Several useful results on the functional space of such functions like completeness and composition theorems are established. By using the fixed-point theorem and some properties of the doubly measure pseudo almost automorphic functions, a set of sufficient criteria are established to ensure the existence, uniqueness and global exponential stability of a $(\mu ,\nu )$-pseudo almost automorphic solution. The theory of this work generalizes the classical results on weighted pseudo almost automorphic functions. Finally, a numerical example is provided to illustrate the validity of the proposed theoretical results

Global dissipativity of fuzzy bidirectional associative memory neural networks with proportional delays

This article aimed to investigate the problem of global dissipativity of Fuzzy Bidirectional Associative Memory Neural Networks (FBAMNNs- for short) with proportional delays. Via Lyapunov Functionals (LFs- for short) and Linear Matrix Inequality (LMI- for short) approach, we obtained new sufficient conditions to guarantee the global dissipativity and global exponential dissipativity of the proposed model. In addition, two different types of activation functions are considered, including general bounded and Lipschitz-type activation functions. Moreover, the globally attractive and globally exponentially attractive sets are presented. Lastly, two numerical examples are given to illustrate the effectiveness of the developed results.Scopu

Global dissipativity of high-order Hopfield bidirectional associative memory neural networks with mixed delays

In this paper, the problem of the global dissipativity of high-order Hopfield bidirectional associative memory neural networks with time-varying coefficients and distributed delays is discussed. By using Lyapunov?Krasovskii functional method, inequality techniques and linear matrix inequalities, a novel set of sufficient conditions for global dissipativity and global exponential dissipativity for the addressed system is developed. Further, the estimations of the positive invariant set, globally attractive set and globally exponentially attractive set are found. Finally, two examples with numerical simulations are provided to support the feasibility of the theoretical findings.Scopu