Outputs Of Nodes Research Articles

Adaptive memory event-triggered observer-based pinning synchronization control for complex dynamical networks under asynchronous attacks

This article addresses the security synchronization issue of complex dynamical networks under asynchronous cyber and physical attacks within the context of cyber-physical systems. An observer is devised to estimate the system's state using the measured outputs of partial nodes. The integration of a buffer for storing historical information enhances the accuracy of state estimation under asynchronous attacks, enabling the observer-based control scheme to generate more precise control signals during attacks. The robustness against external disturbances is reinforced by introducing disturbance compensation for both the observer and the controller. Additionally, an adaptive memory event-triggered mechanism is designed to conserve network resources. Then, a sufficient condition for the synchronization of complex dynamical networks against asynchronous attacks is obtained, and the gains of the observer and controller are computed based on the linear matrix inequality technique. Finally, a simulated example demonstrates the effectiveness of the suggested strategy.

Inferring connectivity of an oscillatory network via the phase dynamics reconstruction

We review an approach for reconstructing oscillatory networks’ undirected and directed connectivity from data. The technique relies on inferring the phase dynamics model. The central assumption is that we observe the outputs of all network nodes. We distinguish between two cases. In the first one, the observed signals represent smooth oscillations, while in the second one, the data are pulse-like and can be viewed as point processes. For the first case, we discuss estimating the true phase from a scalar signal, exploiting the protophase-to-phase transformation. With the phases at hand, pairwise and triplet synchronization indices can characterize the undirected connectivity. Next, we demonstrate how to infer the general form of the coupling functions for two or three oscillators and how to use these functions to quantify the directional links. We proceed with a different treatment of networks with more than three nodes. We discuss the difference between the structural and effective phase connectivity that emerges due to high-order terms in the coupling functions. For the second case of point-process data, we use the instants of spikes to infer the phase dynamics model in the Winfree form directly. This way, we obtain the network’s coupling matrix in the first approximation in the coupling strength.

КОД С СУММИРОВАНИЕМ НА ОСНОВЕ ПРИНЦИПА ВЗВЕШИВАНИЯ ПЕРЕХОДОВ МЕЖДУ ГРУППАМИ РАЗРЯДОВ ИНФОРМАЦИОННОГО ВЕКТОРА

The problem of code modification with summation of weighted transitions is considered. A method for designing summation codes is proposed, which is based on summing the weight coefficients attributed to groups of several digits in the information vector. It is proposed to choose weight coefficients from a sequence of natural numbers. The modified code with the summation of weighted transitions can be used in the construction of devices with the detection of malfunctions and errors in calculations at the outputs of blocks and nodes.

Partial-nodes-based state estimation for linear complex networks with randomly occurring sensor delay and stochastic coupling strength

This paper is concerned with the partial-nodes-based state estimation (PNBSE) problem for a class of time-varying complex networks with randomly occurring sensor delay (ROSD) and stochastic coupling strength (SCS). We assume that only partial outputs of nodes can be measured and utilized in the estimation algorithm design. The ROSD is expressed by a set of Bernoulli distributed random variables and the occurrence probabilities are certain. Moreover, the SCS is represented by a set of random variables which obeys the uniform distribution. The aim of this paper is to design a recursive state estimator based on the framework of the Kalman filter, where the optimization problem of the upper bound of the estimation error covariance (EEC) is discussed by employing the variance-constrained method. It can be seen that the gain matrix of each node can be obtained by solving two Riccati-like difference equations. In addition, the performance of the PNBSE method is characterized by discussing the relationship between the occurrence probabilities of ROSD and the upper bound of the EEC, where the related mathematical proof is provided. Finally, some simulations are given to show the validity and correctness of the presented PNBSE approach.

Scalable consensus filtering for uncertain systems over sensor networks with Round‐Robin protocol

AbstractThis article is concerned with the scalable distributed H∞‐consensus filtering problem for a class of discrete time‐varying systems over sensor networks with the Round‐Robin protocol (RRP). The challenge comes from the fact that the time‐varying parameters of the network are subject to randomly occurring norm‐bounded uncertainties and the measurement outputs of the sensor nodes are saturated due to the sector nonlinearities. For preventing data collisions and saving energy, the RRP determines which neighboring node can access the shared network for information transmission at each time step. An H∞ performance index is proposed to characterize the disturbance attenuation level of the resulting filtering error dynamics. By stochastic analysis in combination with the recursive matrix inequality approach, a distributed filtering algorithm is developed for each individual sensor node to ensure the prespecified estimation performance. Finally, an illustrative simulation example is shown to verify the effectiveness and applicability of the theoretical results.

Finite-horizon H ∞ state estimation for time-varying complex networks based on the outputs of partial nodes

In this paper, the partial-nodes-based resilient filtering problem for a class of discrete time-varying complex networks is investigated. In order to reduce the effect of imprecision of filter parameters on estimation performance, a set of resilient filters is proposed. The measurement output from all network nodes may not be available in the actual system, but only from a fraction of nodes. The state estimators are designed for the time-varying complex network based on partial nodes to make the estimation error achieve the performance constraint over a finite horizon. By employing the completing-the-square technique and the backward recursive Riccati difference equations, the sufficient conditions for the existence of the estimator are derived. Then the gain of the estimator is calculated. Finally, a numerical example is provided to illustrate the effectiveness of the proposed method.

Rule Extraction from Neural Network Using Input Data Ranges Recursively

Neural network is one of the best tools for data mining tasks due to its high accuracy. However, one of the drawbacks of neural network is its black box nature. This limitation makes neural network useless for many applications which require transparency in their decision-making process. Many algorithms have been proposed to overcome this drawback by extracting transparent rules from neural network, but still researchers are in search for algorithms that can generate more accurate and simple rules. Therefore, this paper proposes a rule extraction algorithm named Eclectic Rule Extraction from Neural Network Recursively (ERENNR), with the aim to generate simple and accurate rules. ERENNR algorithm extracts symbolic classification rules from a single-layer feed-forward neural network. The novelty of this algorithm lies in its procedure of analyzing the nodes of the network. It analyzes a hidden node based on data ranges of input attributes with respect to its output and analyzes an output node using logical combination of the outputs of hidden nodes with respect to output class. And finally it generates a rule set by proceeding in a backward direction starting from the output layer. For each rule in the set, it repeats the whole process of rule extraction if the rule satisfies certain criteria. The algorithm is validated with eleven benchmark datasets. Experimental results show that the generated rules are simple and accurate.

Wind Power Prediction Based on Nonlinear Partial Least Square

Wind power prediction is important for the smart grid safe operation and scheduling, and it can improve the economic and technical penetration of wind energy. The intermittent and the randomness of wind would affect the accuracy of prediction. According to the sequence correlation between wind speed and wind power data, we propose a method for short-term wind power prediction. The proposed method adopts the wind speed in every sliding data window to obtain the continuous prediction of wind power. Then, the nonlinear partial least square is adopted to map the wind speed under the time series to wind power. The model carries the neural network as the nonlinear function to describe the inner relation, and the outputs of hidden layer nodes are the extension term of the original independent input matrix to partial least squares regression. To verify the effectiveness of the proposed algorithm, the real data of wind power with different working conditions are adopted in experiments. The proposed method, backpropagation neural network, radial basis function neural network, support vector machine, and partial least square are performed on the real data and their effectiveness is compared. The experimental results show that the proposed algorithm has higher precision, and the real power running curves also verify that the proposed method can predict the wind power in short-term effectively.

Recursive state estimation based-on the outputs of partial nodes for discrete-time stochastic complex networks with switched topology

In this paper, the state estimation problem is studied for a class of discrete-time stochastic complex networks with switched topology. In the network under consideration, we assume that measurement outputs can be got from only partial nodes, besides, the switching rule of this network is characterized by a sequence of Bernoulli random variables. The aim of the presented estimation problem is to develop a recursive estimator based on the framework of extended Kalman filter (EKF), such that the upper bound for the filtering error convariance is optimized. In order to address the nonlinear functions, the Taylor series expansion is utilized and the high-order terms of linearization errors are expressed in an exact way. Furthermore, by solving two Ricatti-like difference equations, the gain matrix can be acquired at each time instant. It is shown that the filtering error is bounded in mean square under some conditions with the aid of stochastic analysis techniques. A numerical example is given to demonstrate the validity of the proposed estimator.

Individual position diversity in dependence socioeconomic networks increases economic output

The availability of big data recorded from massively multiplayer online role-playing games (MMORPGs) allows us to gain a deeper understanding of the potential connection between individuals’ network positions and their economic outputs. We use a statistical filtering method to construct dependence networks from weighted friendship networks of individuals. We investigate the 30 distinct motif positions in the 13 directed triadic motifs which represent microscopic dependences among individuals. Based on the structural similarity of motif positions, we further classify individuals into different groups. The node position diversity of individuals is found to be positively correlated with their economic outputs. We also find that the economic outputs of leaf nodes are significantly lower than that of the other nodes in the same motif. Our findings shed light on understanding the influence of network structure on economic activities and outputs in socioeconomic systems.

Cooperative output regulation of multi-agent systems coupled by dynamic edges

This paper investigates a new class of linear multi-agent systems, in which nodes are coupled by dynamic edges in the sense that each edge has a dynamic system attached. The outputs of the edge dynamic systems combine to form the external input of the node dynamic system, which is called neighboring input; while the outputs of the node dynamic systems are inputs of the edge dynamic systems that therefore can not be directly controlled. Distributed controllers for nodes are presented to realize output synchronization and output cooperation. Output cooperation makes the outputs of nodes realize some cooperation that here is specified as making the neighboring input track a predefined trajectory. The controllers depending on local state and neighboring inputs are designed by combining the feedback passivity theory and the internal model principle. A simulation example on the cooperated current control of an electrical network illustrates the potential applications of the analytic results.

A Component Prediction Method for Flue Gas of Natural Gas Combustion Based on Nonlinear Partial Least Squares Method

Quantitative analysis for the flue gas of natural gas-fired generator is significant for energy conservation and emission reduction. The traditional partial least squares method may not deal with the nonlinear problems effectively. In the paper, a nonlinear partial least squares method with extended input based on radial basis function neural network (RBFNN) is used for components prediction of flue gas. For the proposed method, the original independent input matrix is the input of RBFNN and the outputs of hidden layer nodes of RBFNN are the extension term of the original independent input matrix. Then, the partial least squares regression is performed on the extended input matrix and the output matrix to establish the components prediction model of flue gas. A near-infrared spectral dataset of flue gas of natural gas combustion is used for estimating the effectiveness of the proposed method compared with PLS. The experiments results show that the root-mean-square errors of prediction values of the proposed method for methane, carbon monoxide, and carbon dioxide are, respectively, reduced by 4.74%, 21.76%, and 5.32% compared to those of PLS. Hence, the proposed method has higher predictive capabilities and better robustness.

The space between: negotiating the contours of nodal security governance through ‘Safer Communities’ in Bosnia–Herzegovina

In this article, I analyse three months of ethnographic field work conducted with the United Nations Development Programme's (UNDP) ‘Safer Communities’ project in Bosnia–Herzegovina (BiH) in 2011 using an analytical framework grounded in Lendvai and Stubbs' work on ‘policy translation’. This framework suggests that the spaces which exist between different ‘security nodes’ such as ‘Safer Communities’ can be analysed as ‘contact zones’ where different actors and interests converge to shape the contours of security governance in transitional, post-conflict societies. Analysing ‘Safer Communities’ as a ‘contact zone’ provides insight into the power politics of the project and into the important role that capital and nodal proximity play in determining the translational capacities of different stakeholders. My analysis of this case study affirms the significant influence of supranational institutions like the European Commission and their ability to draw upon substantial economic capital to align the outputs of local security nodes from a distance. It also presents a nuanced account of networked security governance in which multi-lateral institutions like the UNDP can draw upon their nodal proximity and limited capital to mediate pressures for structural alignment. This latter finding is promising because it highlights a ‘nodal solution’ to the question of ‘how nodal relations could be transformed to improve governance processes and outcomes for weak actors’ in structurally weak and dependent societies like BiH.

Enhancing the generalization ability of neural networks through controlling the hidden layers

In this paper we proposed two new variants of backpropagation algorithm. The common point of these two new algorithms is that the outputs of nodes in the hidden layers are controlled with the aim to solve the moving target problem and the distributed weights problem. One algorithm ( AlgoRobust) is not so insensitive to the noises in the data, the second one ( AlgoGS) is through using Gauss–Schmidt algorithm to determine in each epoch which weight should be updated, while the other weights are kept unchanged in this epoch. In this way a better generalization can be obtained. Some theoretical explanations are also provided. In addition, simulation comparisons are made between Gaussian regularizer, optimal brain damage (OBD) and the proposed algorithms. Simulation results confirm that the new proposed algorithms perform better than that of Gaussian regularizer, and the first algorithm AlgoRobust performs better than the second algorithm AlgoGS in the noisy data. On the other hand AlgoGS performs better than the AlgoRobust on the data without noise and the final structure obtained by two new algorithms is comparable to that obtained by using OBD.

A Recursive Nonlinear PLS Algorithm for Adaptive Nonlinear Process Modeling

Partial least squares (PLS) regression has been shown to be a powerful multivariate linear regression method for problems where the data are noisy and highly correlated. However, in many practical situations, the processes being modeled exhibit nonlinear behavior, which cannot be reliably modeled by linear regression methods. Furthermore, the processes often experience time-varying changes. In this paper, a recursive nonlinear PLS (RNPLS) algorithm is proposed to deal with this problem. First, a nonlinear PLS (NLPLS) model is built by performing PLS regression on the extended input matrix and the output matrix, where the extension of the input matrix includes the outputs of the hidden nodes of an RBF network and a constant column with all elements being one. When new data cannot be described by the old model in the sense that the model performance on a moving window of data is not satisfactory, the recursive algorithm is then used to modify the structure and parameters of the model to adapt process changes. Applications of this RNPLS algorithm to a simulated pH neutralization process and an industrial propylene polymerization process are presented and the results demonstrate that this algorithm adapts the process changes effectively and gives satisfactory prediction results.

A functions localized neural network with branch gates

In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network. FLN-bg is applied to function approximation problems and a two-spiral problem. The simulation results show that FLN-bg exhibits better performance than conventional neural networks with comparable complexity.

Interpretation and improvement of an artificial neural network MIR calibration

This work investigates whether useful information can be extracted from a minute analysis of the parameters of a trained artificial neural network (NN). Understanding the data processing that are performed is very useful for the optimisation and validation of neural network multivariate models, particularly if it compensates for their “black box” drawbacks. We focused on the development of calibration models to predict the degree of hydrolysis of bovine hemoglobin from on line mid-infrared (MIR) spectra recorded in a batch reactor. The situation is challenging since the trained network architecture has to model a clustered data set where relationships among the data clearly deviate from the ideal linear situation. Through the analysis of the transfer functions, activation and outputs of the hidden nodes, we show (1) how the nonlinear aspect of the data is processed and (2) how the strong clustering of the data set is considered. The data representations in the hidden layer present meaningful abstraction levels for the analysis of the learning performed. Beyond these results, the major improvement is to help decide the choice of architecture that will be provided through understanding the role played by each unit. The size of the hidden layer that seems the most suitable for generalisation is chosen despite that the root mean squared (RMS) prediction error is not the lowest possible.

Integrated diagnosis using information-gain-weighted radial basis function neural networks

A new approach, the information-gain-weighted radial basis function neural network (RBFNN), has been proposed for machinery diagnosis in a manufacturing environment. This approach is based on the composite neural network, in which a series of RBFNNs are integrated together to perform the task of classification. Each RBFNN has only one output node and is treated as a sub-network. Unlike the conventional RBFNNs, in which only the outputs of hidden nodes are weighted, the information-gain-weighted RBFNN uses a weighting vector also in its input layer. In addition, the weighting vector is obtained according to the information gain of each input index to the process of diagnosis. In the diagnosis strategy, one sub-net is responsible for diagnosing one specific fault, while the composite network as a whole can diagnose different kinds of faults. By this approach, classification among known faults can be made and a novel fault, if any, can also be identified. This approach has been tested in the diagnosis of an internal thread tapping process. The results showed that the information-gain-weighted RBFNN can produce better distinction between conditions and the scheme of a composite neural network is indeed an improved structure for machinery diagnosis in the manufacturing environment.

Tone recognition of continuous Mandarin speech assisted with prosodic information

In this paper, a simple recurrent neural network (SRNN) is employed to model the prosody of continuous Mandarin speech to assist tone recognition. For each syllable in continuous speech, several acoustic features carrying prosodic information are extracted and taken as inputs to the SRNN. If proper linguistic features extracted from the context of the syllable are set as output targets, the SRNN can learn to represent the prosodic state of the utterance at the syllable using its hidden nodes. Outputs of the hidden nodes then serve as additional recognition features to assist recognition of the tone of the syllable. The performance of the proposed tone recognition approach was examined by simulation on a multilayer perception (MLP)-based speaker-dependent tone recognition task. The recognition rate was improved from 91.38% to 93.10%. The SRNN prosodic model is further analyzed to exploit the linguistic meaning of prosodic states. By vector quantizing the outputs of the hidden nodes of the SRNN, a finite-state automata that roughly represents the mechanism of human prosody pronunciation can be obtained.