Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that e...
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Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes, where neurons work in parallel in the sense that each neuron that can fire should fire, but the work in each neuron is sequential in the sense that at most one rule can be applied at each computation step. In this work, we consider SN P systems with the restriction that at most one neuron can fire at each step, and each neuron works in an exhaustive manner (a kind of local parallelism - an applicable rule in a neuron is used as many times as possible). Such SN P systems are called sequential SN P systems with exhaustive use of rules. The computation power of sequential SN P systems with exhaustive use of rules is investigated. Specifically, characterizations of Turing computability and of semilinear sets of numbers are obtained, as well as a strict superclass of semilinear sets is generated. The results show that the computation power of sequential SN P systems with exhaustive use of rules is closely related with the types of spiking rules in neurons. (C) 2012 Elsevier Ireland Ltd. All rights reserved.
Feature extraction in brain-computer interface (BCI) work is an important task that significantly affects the success of brain signal classification. In this paper, a feature extraction method of electroencephalograph...
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The protocol stack plays a critical role in determining the performance of Networked control System (NCS), which governs the communication activities and directly affects the communication Quality of Service (QoS). Fu...
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As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human,...
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As the largest microblog service in China, Sina Weibo has attracted numerous automated applications (known as bots) due to its popularity and open architecture. We classify the active users from Sina Weibo into human, bot-based and hybrid groups based solely on the study of temporal features of their posting behavior. The anomalous burstiness parameter and time-interval entropy value are exploited to characterize automation. We also reveal different behavior patterns among the three types of users regarding their reposting ratio, daily rhythm and active days. Our findings may help Sina Weibo manage a better community and should be considered for dynamic models of microblog behaviors. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, the event-triggered leader-following consensus problem of general linear fractional-order multiagent systems subject to input delay on directed graph is investigated. A distributed event-triggered contr...
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In this paper, the event-triggered leader-following consensus problem of general linear fractional-order multiagent systems subject to input delay on directed graph is investigated. A distributed event-triggered control protocol with input delay is proposed firstly. By applying the properties of the Mittag-Leffler function, the technique of inequality, and the matrix theory, some consensus criteria for the fractional-order multiagent systems are obtained. It is shown that the dynamic leader can be tracked by the followers for any bounded delay, and the followers receive and update their control protocol at some discrete time instants. Some simulation results are presented to illustrate the effectiveness of the theoretical analysis.
In this paper, synchronization problem of coupled neural networks with stochastic disturbances and time-delay is analyzed. For the system under study, each subsystem interacts with others in an on-off way which can be...
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In this paper, synchronization problem of coupled neural networks with stochastic disturbances and time-delay is analyzed. For the system under study, each subsystem interacts with others in an on-off way which can be employed to deal with communication congestion in signals transmission. By stochastic analysis techniques, sufficient conditions that guarantee mean square synchronization of the coupled system are established. Moreover, the underlying network needs not be undirected or strongly connected. Finally, some numerical simulations are given to verify the usefulness and effectiveness of our results.
Incremental learning models need to update the categories and their conceptual understanding over time. The current research has placed more emphasis on learning new categories, while another common but under-explored...
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Incremental learning models need to update the categories and their conceptual understanding over time. The current research has placed more emphasis on learning new categories, while another common but under-explored incremental scenario is the updating and refinement of category labels. In this paper, we present the Hierarchical Task-Incremental Learning (HTIL) problem, which emulates the human cognitive process of progressing from coarse to fine. While the model learns the fine categories, it gains a better understanding of the perception of coarse categories, thereby enhancing its ability to differentiate between previously encountered classes. Inspired by neural collapse, we propose to initialize the coarse class prototypes and update the new fine class using hierarchical relations. We conduct experiments on diverse hierarchical data benchmarks, and the experiment results show our method achieves excellent results.
A robust non-linear feedback control strategy combined with neural network (NN) estimator is presented, for the non-linear model of bank-to-turn (BTT) missile with modelling uncertainties. The non-linearities in the m...
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A robust non-linear feedback control strategy combined with neural network (NN) estimator is presented, for the non-linear model of bank-to-turn (BTT) missile with modelling uncertainties. The non-linearities in the missile dynamics are taken into account, including coupling items between three channels. Standard feedback linearisation is implemented to linearise and decouple the nominal system. In the presence of unknown bounded uncertainties, the performance will deteriorate because the precise model cannot be obtained. Then, linear matrix inequality (LMI) based guaranteed cost control (GCC) is adopted to solve the robust control problem for the linearised uncertain models. Further, adaptive neural network estimators, which use Lyapunov based tuning rules, are integrated in the control strategy to eliminate the effect of high-order uncertain terms. Simulation results on a specified BTT missile model are provided to demonstrate the feasibility and effectiveness of the proposed approach, which achieves more improved tracking performance comparing with conventional method.
It is widely observed that life activities are regulated through conformational transitions of biological macromolecules, which inspires the construction of environmental responsive nanomachines in recent years. Here ...
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It is widely observed that life activities are regulated through conformational transitions of biological macromolecules, which inspires the construction of environmental responsive nanomachines in recent years. Here we present a thermal responsive DNA origami dimers system, whose conformations can be cyclically switched by thermal cycling. In our strategy, origami dimers are assembled at high temperatures and disassembled at low temperatures, which is different from the conventional strategy of breaking nanostructures using high temperatures. The advantage of this strategy is that the dimers system can be repeatedly operated without significant performance degradation, compared to traditional strategies such as conformational transitions via i-motif and G-quadruplexes, whose performance degrades with sample dilution due to repeated addition of trigger solutions. The cyclic conformational transitions of the dimers system are verified by fluorescence curves and AFM images. This research offered a new way to construct cyclic transformational nanodevices, such as reusable nanomedicine delivery systems or nanorobots with long service lifetimes.
This paper discusses the recurrent neural network (RNN) with memristors as connection weights. Memristor is a nonlinear resistor. Memristance varies periodically with time when the sinusoidal voltage source is applied...
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This paper discusses the recurrent neural network (RNN) with memristors as connection weights. Memristor is a nonlinear resistor. Memristance varies periodically with time when the sinusoidal voltage source is applied. According to this property of memristor, it shows that coefficients of RNN with memristors are periodic functions with respect to time t. By dividing the state space and using contraction mapping theorem, one sufficient condition is obtained for multiperiodicity. And the periodic orbits located in saturation regions are locally exponentially stable limit cycles. At last, one example is given for verifying the validity of our result.
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