In this paper, inspired by the multiplicative generators of overlap functions, we mainly propose the concepts of multiplicative generator pairs of n-dimensional overlap functions, in order to extend the dimensionality...
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In this paper, inspired by the multiplicative generators of overlap functions, we mainly propose the concepts of multiplicative generator pairs of n-dimensional overlap functions, in order to extend the dimensionality of overlap functions from 2 to n. We present the condition under which the pair (g, h) can multiplicatively generate an n-dimensional overlap function Og,h. we focus on the homogeneity and idempotency property on multiplicatively generated n-dimensional overlap functions.
Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differentia...
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Aimed at improving the insufficient search ability of constraint differential evolution with single constraint handling technique when solving complex optimization problem, this paper proposes a constraint differential evolution algorithm?based on ensemble of constraint handling techniques and multi-population?framework, called ECMPDE. First, handling three improved variants of differential evolution algorithms are dynamically matched with two constraint handling techniques through the constraint allocation mechanism. Each combination includes three variants with corresponding constraint handling technique?and these combinations are in the set. Second, the population is divided into three smaller subpopulations and one larger reward subpopulation. Then a combination with three constraint algorithms is randomly selected from the set, and the three constraint algorithms are run in three sub-populations respectively. According to the improvement of fitness value, the optimal constraint?algorithm is selected to run on the reward sub-population, which can share?information and close cooperation among populations. In order to verify the effectiveness of the proposed algorithm, 12 standard constraint optimization problems?and 10 engineering constraint optimization problems are tested. The experimental results show that ECMPDE is an effective algorithm for solving constraint optimization problems.
Industry 4.0 and Industrial Internet of Things (IIoT) are current trends in the industrial automation world. They require connections of factory networks to the internet. This trend increases the vulnerability of fact...
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ISBN:
(数字)9781728157542
ISBN:
(纸本)9781728157559
Industry 4.0 and Industrial Internet of Things (IIoT) are current trends in the industrial automation world. They require connections of factory networks to the internet. This trend increases the vulnerability of factory networks to attacks. Here, we present an approach that monitors the activities of factory network traffic based on two linear feature extraction algorithms, i.e. LDA and PCA. A Machine-Learning-based approach is used to analyze the records of network connections from the UNSW-NB15 database and to detect and report anomalies such as malicious attacks. The experimental results show the feasibility of the provided method in accuracy, detection rate, and false alarm rate.
In this paper, we presents a fixed-time convergent distributed algorithm to achieve least square solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exc...
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ISBN:
(纸本)9781728101071;9781728101064
In this paper, we presents a fixed-time convergent distributed algorithm to achieve least square solutions of networked linear equations. Each agent in the network only knows a subset of the equations and can only exchange messages with its nearest neighbors. Compared with the finite-time algorithm, it is shown that the settling time is independent to the initial states of the algorithm and can be preassign according to requirements of the task. A numerical example is provided to illustrate the effectiveness of the theoretical result.
Large-scale neural networks have been widely used in data processing applications. As a special type of neural network, the recurrent neural network (RNN) is equipped with additional recurrent connections. This unique...
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Large-scale neural networks have been widely used in data processing applications. As a special type of neural network, the recurrent neural network (RNN) is equipped with additional recurrent connections. This unique architecture enables the RNN to memory the processed information and makes it an expressive model for nonlinear sequence processing tasks. However, the large computation complexity makes it difficult to effectively train an RNN. In this paper, we aim to develop parallel approaches for a particular type of RNN, known as echo state network (ESN). We first decompose the training problem of a large-scale ESN into a number of smaller subproblems. Next, we introduce the alternating direction method of multipliers (ADMM) to solve the optimization problem. Then, two parallel algorithms are proposed to train the ESN across the learning agents, which restricting communication to the fusion of achieving an overall decision. The experiment results of the prediction of the Mackey-Glass chaotic time-series demonstrate that the proposed algorithms for a large-scale ESN are able to reach a comparable accuracy achieved by a single ESN on the same dataset.
This work is the first to consider the self-triggered distributed optimization problem for multi-agent systems with periodic sampling communication. Each agent computes its next update time instance at the previous ti...
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This work is the first to consider the self-triggered distributed optimization problem for multi-agent systems with periodic sampling communication. Each agent computes its next update time instance at the previous time. The self-triggered distributed protocols are proposed to reduce the load of in-network communication. It is proved that these self-triggered protocol achieves optimal consensus exponentially under a undirected and connected network topology.
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