Size-dependent hygro–thermo–electro–mechanical vibration analysis of functionally graded piezoelectric nanobeams resting on Winkler–Pasternak foundation undergoing preload and magnetic field

Microsystem Technologies - In this study, nonlocal beam theory is utilized for vibration analysis of hygro–electro–thermo–mechanical of functionally graded material (FGM) nanobeam...     

Balancing exploration and exploitation in memetic algorithms: A learning automata approach

One of the problems with traditional genetic algorithms (GAs) is premature convergence, which makes them incapable of finding good solutions to the problem. The memetic algorithm (MA) is an extension of the GA. It uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, we introduce a new algorithm based on learning automata (LAs) and an MA, and we refer to it as LA‐MA. This algorithm is composed of 2 parts: a genetic section and a memetic section. Evolution is performed in the genetic section, and local search is performed in the memetic section. The basic idea of LA‐MA is to use LAs during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. For this purpose, we present a criterion for the estimation of success of the local search at each generation. This criterion is used to calculate the probability of applying the local search to each chromosome. We show that in practice, the proposed probabilistic measure can be estimated reliably. On the basis of the relationship between the genetic section and the memetic section, 3 versions of LA‐MA are introduced. LLA‐MA behaves according to the Lamarckian learning model, BLA‐MA behaves according to the Baldwinian learning model, and HLA‐MA behaves according to both the Baldwinian and Lamarckian learning models. To evaluate the efficiency of these algorithms, they have been used to solve the graph isomorphism problem. The results of computer experimentations have shown that all the proposed algorithms outperform the existing algorithms in terms of quality of solution and rate of convergence.     

Observer‐based tracking control for switched stochastic systems based on a hybrid 2‐D model

This paper is concerned with the observer‐based output tracking problem for a class of linear switched stochastic systems with time delay and disturbance by using repetitive control approach. More precisely, a two‐dimensional hybrid model is incorporated to obtain and optimize the repetitive controller. In particular, the repetitive controller is used to improve the tracking performance through its continuous learning actions. In addition, an equivalent‐input‐disturbance estimator is incorporated into the repetitive control design approach to reduce the effect of the external disturbances. The main aim of the control design is to track the periodic reference signal with the measured output of the system under consideration even in the presence of an unknown bounded disturbance. By constructing a suitable Lyapunov‐Krasovskii functional and using average dwell time approach and Jensen inequality, sufficient conditions are obtained in terms of linear matrix inequalities to guarantee the mean‐square exponential stability of the considered system. Eventually, a numerical example is provided to demonstrate the effectiveness of the developed method.     

New delay range–dependent stability criteria for interval time‐varying delay systems via Wirtinger‐based inequalities

Stability conditions for time‐delay systems using the Lyapunov‐based methodologies are generically expressed in terms of linear matrix inequalities. However, due to assuming restrictive conditions in deriving the linear matrix inequalities, the established stability conditions can be strictly conservative. This paper attempts to relax this problem for linear systems with interval time‐varying delays. A double‐integral inequality is derived inspired by Wirtinger‐based single‐integral inequality. Using the advanced integral inequalities, the reciprocally convex combination techniques and necessary slack variables, together with extracting a condition for the positive definiteness of the Lyapunov functional, novel stability criteria, have been established for the system. The effectiveness of the criteria is evaluated via 2 numerical examples. The results indicate that more complex stability criteria not only improve the stability region but also bring computational expenses.     

Composite hierarchical antidisturbance control for a class of discrete‐time stochastic systems

In this paper, antidisturbance control and estimation problem are discussed for a class of discrete‐time stochastic systems with nonlinearity and multiple disturbances, which include the disturbance with partially known information and a sequence of random vectors. A disturbance observer is constructed to estimate the disturbance with partially known information. A composite hierarchical antidisturbance control scheme is proposed by combining disturbance observer and H_∞ control. It is proved that the 2 different disturbances can be rejected and attenuated, and the corresponding desired performances can be guaranteed for discrete‐time stochastic systems with known and unknown nonlinear dynamics, respectively. Simulation examples are given to demonstrate the effectiveness of the proposed scheme.     

Global output feedback control for a class of nonlinear systems with unknown homogenous growth condition

This paper investigates the problem of global control for a class of nonlinear systems via output feedback. The system nonlinearities satisfy the homogenous growth condition with unknown growth rate. First, a homogenous observer is constructed for estimating the system state. Then, two novel dynamic gains are presented to deal with the unknown growth rate. Subsequently, by adding a power integrator technique, a dynamic output feedback controller is designed to guarantee that all the signals of the closed‐loop system are bounded and the system states globally converge to origin. Finally, an example is provided to illustrate the validity of the proposed control scheme.     

Prescribed performance adaptive neural output control for a class of switched nonstrict‐feedback nonlinear time‐delay systems: State‐dependent switching law approach

This paper investigates the tracking problem for a class of uncertain switched nonlinear delayed systems with nonstrict‐feedback form. To address this problem, by introducing a new common Lyapunov function (CLF), an adaptive neural network dynamic surface control is proposed. The state‐dependent switching rule is designed to orchestrate which subsystem is active at each time instance. In order to compensate unknown delay terms, an appropriate Lyapunov‐Krasovskii functional is considered in the constructing of the CLF. In addition, a novel switched neural network–based observer is constructed to estimate system states through the output signal. To maintain the tracking error performance within a predefined bound, a prescribed performance bound approach is employed. It is proved that by the proposed output‐feedback control, all the signals of the closed‐loop system are bounded under the switching law. Moreover, the transient and steady‐state tracking performance is guaranteed by the prescribed performance bound. Finally, the effectiveness of the proposed method is illustrated by two numerical and practical examples.     

DAWA: Defending against wormhole attack in MANETs by using fuzzy logic and artificial immune system

Mobile ad hoc networks (MANETs) are mobile networks, which are automatically outspread on a geographically limited region, without requiring any preexisting infrastructure. Mostly, nodes are both self-governed and self-organized without requiring a central monitoring. Because of their distributed characteristic, MANETs are vulnerable to a particular routing misbehavior, called wormhole attack. In wormhole attack, one attacker node tunnels packet from its position to the other attacker nodes. Such wormhole attack results in a fake route with fewer hop count. If source node selects this fictitious route, attacker nodes have the options of delivering the packets or dropping them. For this reason, this paper proposes an improvement over AODV routing protocol to design a wormhole-immune routing protocol. The proposed protocol called defending against wormhole attack (DAWA) employs fuzzy logic system and artificial immune system to defend against wormhole attacks. DAWA is evaluated through extensive simulations in the NS-2 environment. The results show that DAWA outperforms other existing solutions in terms of false negative ratio, false positive ratio, detection ratio, packet delivery ratio, packets loss ratio and packets drop ratio.     

Distributed cooperative target detection and localization in decentralized wireless sensor networks

In many network applications such as surveillance systems, it is crucial to detect the target and estimate its location. Distributed processing algorithms are capable of providing fast, secure, scalable and robust solutions. In this paper, we study the problem of target detection and localization in a wireless sensor network. Most of the current researches have focused on centralized algorithms and the works done on distributed algorithms usually need center assistance and practical issues such as communication link failure is not addressed in them. In this paper, we first propose a distributed consensus-based algorithm for target detection and then propose a distributed consensus-based localization algorithm. We assume that the target transmits a radio signal that is received in sensors equipped with limited computational and power resources. We consider the communication link failure and use the collaboration of sensor nodes to detect the presence of target. In the proposed target localization algorithm, sensor nodes estimate their distance toward the target using the received signal strength. In both the proposed algorithms, sensor nodes exchange information only with their neighbors and each makes an individual decision. We further prove the convergence of the proposed algorithms. Computer simulations confirm that the proposed algorithms are very fast and applicable in high-performance networks. We improve the localization accuracy at least by 25 % in terms of localization error compared with some recent algorithms.     

An MLP-based representation of neural tensor networks for the RDF data models

In this paper, a new representation of neural tensor networks is presented. Recently, state-of-the-art neural tensor networks have been introduced to complete RDF knowledge bases. However, mathematical model representation of these networks is still a challenging problem, due to tensor parameters. To solve this problem, it is proposed that these networks can be represented as two-layer perceptron network. To complete the network topology, the traditional gradient based learning rule is then developed. It should be mentioned that for tensor networks there have been developed some learning rules which are complex in nature due to the complexity of the objective function used. Indeed, this paper is aimed to show that the tensor network can be viewed and represented by the two-layer feedforward neural network in its traditional form. The simulation results presented in the paper easily verify this claim.