Uncertain Networks Research Articles

Developing a two-phase model to configure uncertain knowledge-based networks

The purpose of the paper is to develop a bi-objective mathematical programming model to design knowledge-based networks with respect to both configuration costs and suppliers' risks minimisation. The study utilises a mixed-integer linear programming model addressing both the deterministic and robust optimisation approaches. The performance and applicability of the model has been tested by a case study in Iran. The comparison between the deterministic and robust optimisation models indicated that the robust model could efficiently deal with uncertainties associated with costs and demand amount. The given model can be applied for NPD and knowledge-based networks, in which the sustainable collaborations with suppliers is accounted for a critical issue. The potential suppliers can be evaluated with respect to their strength and weakness points in running a steady collaboration. It is required to establish reliable relations between the members of a knowledge-based network to strengthen further interactions, investigated here by a two-phase model.

Localization Algorithm for Mobile Nodes in Wireless Sensor Networks Based on Discrete-Time H∞ Filtering and Dynamic Node Model

Precise localization of mobile nodes in uncertain conditions is a fundamental and crucial topic in wireless sensor networks. In this paper a discrete-time H∞ filtering and dynamic node model based algorithm is proposed. Accurate and complicate network models are not required and no assumptions are needed for the external noise characteristics but only have to be seen as energy-limited. State and measurement equation of unknown node are built with basic kinematic property and sensor node measurement method, including the impact of environment random uncertainties and node connection failure. The position of the mobile node is estimated by the filter using an integration of position information from other assisting nodes. Complying to Linear Matrix Inequality (LMI) criterion, a theorem of H∞ filter designed for stochastic uncertain network is given. From the dynamics of the node, the solution existence of the proposed filter is obtained, and a low computational complexity method to get the optimum solution from the filter is provided in a simple motion model. The simulation results show that this method not only can achieve highly reliability but also the better localization accuracy under stochastic uncertainty wireless sensor network (WSN) conditions compared with the classic mobile MCL and MCB mobile schemes.

Distributed Dynamic Traffic Modeling and Implementation Oriented Different Levels of Induced Travelers

In order to respond to the variable state of traffic network in time, a distributed dynamic traffic assignment strategy is proposed which can improve the intelligent traffic management. The proposed dynamic assignment method is based on utility theory and is oriented to different levels of induced users. A distributed model based on the marginal utility is developed which combines the advantages of both decentralized paradigm and traveler preference, so as to provide efficient and robust dynamic traffic assignment solutions under uncertain network conditions. Then, the solution algorithm including subroute update and subroute calculation is proposed. To testify the effectiveness of the proposed model in optimizing traffic network operation and minimizing traveler’s cost on different induced levels, a sequence numerical experiment is conducted. In the experiment, there are two test environments: one is in different network load conditions and the other is in different deployment coverage of local agents. The numerical results show that the proposed model not only can improve the running efficiency of road network but also can significantly decrease the average travel time.

Uncertain Network Public Sentiment Emergency Decision Model Based on Grey Relational Degree

This paper presents a novel warning decision model for uncertain network public sentiment emergency of which index assessments are provided as uncertain values. First, a weighted grey relational formula is proposed to compute the comprehensive grey relational degree between the unknown network public sentiment emergency and each original emergency case. According to the evaluated grey relation degree of all the candidate cases, we can eas- ily rank all the matched cases and get the most optimal source case. Also the related emergency section may use or modify the corresponding decision solution of the most matched historical case to facilitate the urgent decision- making of current network public sentiment emergency with incomplete information.

A bi-level decision support system for uncertain network design with equilibrium flow

A bi-level decision support system (BDSS) is proposed for a normative road network design with uncertain travel demand. A bi-level decision support model with link capacity expansion is developed to simultaneously reduce travel delay to road users and mitigate vulnerability of road network. A tractable solution scheme for BDSS is developed. Due to some hierarchy in decision-making order of BDSS, a bi-level programming is employed. A risk-averse Stackelberg solution is established for a normative BDSS under travel demand uncertainty. Numerical computations are performed using a real-data road network. Computational results indicate that the proposed solution scheme can effectively improve a worst-case performance of BDSS with greater success while incurring a relatively slighter loss of optimality when compared to deterministic solutions at nominal condition. Particularly, our computation results showed that proposed solution becomes more attractive as the realization taken by unknown demand growth factor increases.

Adaptive Synchronization of Fractional Neural Networks with Unknown Parameters and Time Delays

In this paper, the parameters identification and synchronization problem of fractional-order neural networks with time delays are investigated. Based on some analytical techniques and an adaptive control method, a simple adaptive synchronization controller and parameter update laws are designed to synchronize two uncertain complex networks with time delays. Besides, the system parameters in the uncertain network can be identified in the process of synchronization. To demonstrate the validity of the proposed method, several illustrative examples are presented.

Extrapolating Validation Using Bayesian Networks with Interval Probabilities

This paper develops a model validation method in the case of the full scale tests for the system model are infeasible. The Bayesian network with uncertain conditional probability parameters is used to represent the relations between the large computational model and its smaller modules. The interval probability theory is adopted to extrapolate the posterior probability of the interested variable in the uncertain Bayesian network. An interval valued Bayes factor is obtained to be the metric for model validation.

Learning with handoff cost constraint for network selection in heterogeneous wireless networks

AbstractIn heterogeneous wireless networks, network selection algorithms provide the user with the optimum network access choice. The optimal network is evaluated according to network parameters. Considering that the network parameters are dynamic and unavailable for the user in realistic heterogeneous wireless network environments, most existing network selection algorithms cannot work effectively. Learning‐based algorithms can address the problem of uncertain network parameters, while they commonly need considerable network handoff, resulting in unbearable handoff cost. In order to tackle the uncertainty of network parameters, we formulate the network selection problem as a multi‐armed bandit problem. Moreover, two online learning‐based network selection algorithms with a special consideration on reducing network handoff cost are proposed. By updating in a block manner, both algorithms achieve optimal logarithmic‐order regret and limited network handoff cost. The simulation indicates that the two algorithms can significantly reduce the network handoff cost and improve the transmission performance compared with existing algorithms, simultaneously. Copyright © 2014 John Wiley & Sons, Ltd.

Centralized predictive controller design based on an uncertain neural network for multivariable non-linear systems

In this paper, we propose a feedforward neural networks-based robust predictive controller for a class of multi-input–multi-output non-linear systems. Using the structured uncertainties of the output layer’s weights of the neural networks model, the non-linear model of the real system is determined at each operating point. The control law is formulated as a minimax problem, which is solved online. The non-convex optimization is developed by minimizing the worst case of the objective cost function, taking into account the uncertainties of the non-linear model and the input control signal constraints. The efficiency of the proposed neural predictive controller is illustrated, in simulation, with a multivariable system example.

Synchronization of a class of uncertain stochastic discrete-time delayed neural networks

Abstract The global asymptotical synchronization problem is discussed for a general class of uncertain stochastic discrete-time neural networks with time delay in this paper. Time delays include time-varying delay and distributed delay. Based on the drive-response concept and the Lyapunov stability theorem, a linear matrix inequality (LMI) approach is given to establish sufficient conditions under which the considered neural networks are globally asymptotically synchronized in the mean square. Therefore, the global asymptotical synchronization of the stochastic discrete-time neural networks can easily be checked by utilizing the numerically efficient Matlab LMI toolbox. Moreover, the obtained results are dependent not only on the lower bound but also on the upper bound of the time-varying delays, that is, they are delay-dependent. And finally, a simulation example is given to illustrate the effectiveness of the proposed synchronization scheme.

Predictive Method of Networked Control of Industrial Automation Systems

A New method for real-time prediction of uncertain network transmission time delay and the closed-loop control method through the network manufacturing and industrial plant are introduced. Put forward delay prediction method is based on the multilayer perceptron neural model. To reduce the amount of the first layer of neurons network, thus reducing the computational burden a real-time implementation, a method for the determination of Markov delay sequence order. Used to predict delay and a zero order equivalent discrete time model of the plant, a kind of time-varying state feedback control algorithm is put forward a strategy to obtain real-time update. The stability of the closed-loop switch sufficient conditions is derived using the theorem of linear system. It shows that the method studies of industrial network by two cases, i.e., a DC motor driven transport roll paper and a milling machine. Simulation study describes the effectiveness of the method to control these challenging problems.

Applied-Information Technology in Robust Synchronization of Uncertain Fractional-Order Complex Networks

This paper deals with robust synchronization of a class of uncertain fractional-order complex networks. Robust stability criteria of uncertain fractional-order system with is investigated based on LMI technique. Furthermore, robust synchronization of uncertain fractional-order complex networks is discussed based on the LMI technique. Finally, numerical example is given to illustrate the effectiveness of the proposed methods.

Chance distribution of the maximum flow of uncertain random network

A network is called uncertain random network if some arc capacities of the network are uncertain variables and others are random variables. The main purpose of this paper is to study the maximum flow in an uncertain random network. Under the framework of chance theory, this paper obtains chance distribution of the maximum flow of an uncertain random network. At the same time, the expected value of maximum flow is given for an uncertain random network. A new method is derived to calculate chance distribution of the maximum flow for an uncertain random network.

Spatiotemporal chaos synchronization between uncertain complex networks with diverse structures

Spatiotemporal chaos synchronization between uncertain complex networks with diverse structures is investigated. The identification law of unknown parameters and the adaptive law of the configuration matrix element in state equations of network nodes are determined based on stability theory, and the conditions of realizing spatiotemporal chaos synchronization between uncertain complex networks with different structures are discussed and obtained. Further, the Fisher–Kolmogorov system with spatiotemporal chaotic behavior is taken as the nodes of drive and response networks to imitate the experiment. It is found that the synchronization performance between two networks is very stable.

New Criteria on Delay-Dependent Robust Stability for Uncertain Markovian Stochastic Delayed Neural Networks

This paper is mainly concerned about the problem of the robustly exponential stability of uncertain stochastic neural networks with time-varying delay and Markovian jump parameters. Some new delay-dependent stability criteria are established for the considered systems by constructing a modified Lyapunov---rasovskii functional, which are expressed in terms of linear matrix inequalities. Compared with some existing ones, our obtained results have a potential less conservatism. Finally, three illustrative numerical examples are provided to show the effectiveness of the obtained results.

Uncertain Networks Optimization Based on Random ADD Algorithm

In order to solve the traditional network optimization problem, the dual gradient descent algorithm is adopted. Although the algorithm can be realized by a distributed method, its convergence rate is slow. Acceleration dual decline algorithm mainly uses the distributed calculation of the approximative Newton step to improve its convergence rate. Due to the uncertainty characteristic of communication networks, its convergence is difficult to be guaranteed in the presence of restriction uncertainty. An ADD algorithm based on random modality is proposed to resolve the network optimization problem. It is proved theoretically that ADD algorithm can converge to an error neighborhood of optimum value with higher probability when the uncertainty mean square error is bounded; the ADD algorithm can also converge to the optimum value with higher probability when there is the more stringent restriction condition. Experiment results demonstrate the convergence rate of the proposed algorithm is more than 100 times faster than the random gradient descent algorithm

Finite-time boundedness for uncertain discrete neural networks with time-delays and Markovian jumps

This paper is concerned with stochastic finite-time boundedness analysis for a class of uncertain discrete-time neural networks with Markovian jump parameters and time-delays. The concepts of stochastic finite-time stability and stochastic finite-time boundedness are first given for neural networks. Then, applying the Lyapunov approach and the linear matrix inequality technique, sufficient criteria on stochastic finite-time boundedness are provided for the class of nominal or uncertain discrete-time neural networks with Markovian jump parameters and time-delays. It is shown that the derived conditions are characterized in terms of the solution to these linear matrix inequalities. Finally, numerical examples are included to illustrate the validity of the presented results.

Dynamic Chinese Restaurant Game: Theory and Application to Cognitive Radio Networks

Users in a social network are usually confronted with decision making under uncertain network state. While there are some works in the social learning literature on how to construct belief on an uncertain network state, few study has been made on integrating learning with decision making for the scenario where users are uncertain about the network state and their decisions influence with each other. Moreover, the population in a social network can be dynamic since users may arrive at or leave the network at any time, which makes the problem even more challenging. In this paper, we propose a Dynamic Chinese Restaurant Game to study how a user in a dynamic social network learns the uncertain network state and make optimal decision by taking into account not only the immediate utility but also subsequent users' negative influence. We introduce a Bayesian learning based method for users to learn the network state, and propose a Multi-dimensional Markov Decision Process based approach for users to achieve the optimal decisions. Finally, we apply the Dynamic Chinese Restaurant Game to cognitive radio networks and demonstrate from simulations to verify the effectiveness and efficiency of the proposed scheme.

Robust Stability for a Class of Uncertain Hopfield Neural Networks of Neutral-type with Time-varying Delays

Robust Stability for a Class of Uncertain Hopfield Neural Networks of Neutral-type with Time-varying Delays

Anti-Synchronization of Markovian Jumping Stochastic Chaotic Neural Networks with Mixed Time Delays

The anti-synchronization control is investigated for a class of uncertain stochastic chaotic neural networks with both Markovian jump parameters and mixed delays. The mixed delays consists of discrete and distributed time-varying delays. First, by combining the Lyapunov method and a generalized Halanay-type inequality for stochastic differential equations, a delay-dependent criterion is established to guarantee the state variables of the discussed stochastic chaotic neural networks to be globally exponential anti-synchronized. Next, by utilizing a novel lemma and the Jensen integral inequality, a delay-dependent criterion is proposed to achieve the globally stochastic robust anti-synchronization. With some parameters being fixed in advance, the proposed conditions are all expressed in terms of linear matrix inequalities, which can be solved numerically by employing the standard Matlab LMI toolbox package. Finally, two examples are proposed to demonstrate the effectiveness and usefulness of the proposed results.