Abstract: Natural selection is the strongest known antientropic process in the universe when operating at the biological level and may also operate at the cosmological level. Consideration of how biological natural selection creates adaptations may illuminate the consequences and significance of cosmological natural selection. An organismal trait is more likely to constitute an adaptation if characterized by more improbable complex order, and such order is the hallmark of biological selection. If the same is true of traits created by selection in general, then the more improbably ordered something is (i.e., the lower its entropy), the more likely it is to be a biological or cosmological adaptation. By this logic, intelligent life (as the least-entropic known entity) is more likely than black holes or anything else to be an adaptation designed by cosmological natural selection. This view contrasts with Smolin’s suggestion that black holes are an adaptation designed by cosmological natural selection and that life is the by-product of selection for black holes. Selection may be the main or only ultimate antientropic process in the universe/multiverse; that is, much or all observed order may ultimately be the product or by-product of biological and cosmological selection. PubDate: Tue, 20 Jun 2017 06:10:02 +000

Abstract: In this paper, we explore an impulsive stochastic infected predator-prey system with Lévy jumps and delays. The main aim of this paper is to investigate the effects of time delays and impulse stochastic interference on dynamics of the predator-prey model. First, we prove some properties of the subsystem of the system. Second, in view of comparison theorem and limit superior theory, we obtain the sufficient conditions for the extinction of this system. Furthermore, persistence in mean of the system is also investigated by using the theory of impulsive stochastic differential equations (ISDE) and delay differential equations (DDE). Finally, we carry out some simulations to verify our main results and explain the biological implications. PubDate: Tue, 20 Jun 2017 00:00:00 +000

Abstract: We consider the random fuzzy differential equations (RFDEs) with impulses. Using Picard method of successive approximations, we shall prove the existence and uniqueness of solutions to RFDEs with impulses under suitable conditions. Some of the properties of solution of RFDEs with impulses are studied. Finally, an example is presented to illustrate the results. PubDate: Mon, 19 Jun 2017 00:00:00 +000

Abstract: Due to the unbalance distribution of network resources and network traffic, congestion is an inherent property of the Internet. The consensus congestion controller based on the multiagent system theory is designed for the multirouter topology, which improves the performance of the whole networks. Based on the analysis of the causes of congestion, the topology of multirouter networks is modeled based on the graph theory and the network congestion control problem is described as a consensus problem in multiagent systems. Simulation results by MATLAB and Ns2 indicate that the proposed algorithm maintains a high throughput and a low packet drip ratio and improves the quality of the service in the complex network environment. PubDate: Mon, 19 Jun 2017 00:00:00 +000

Abstract: The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy -means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time. PubDate: Sun, 18 Jun 2017 08:43:57 +000

Abstract: Recent advances in modern manufacturing industries have created a great need to track and identify objects and parts by obtaining real-time information. One of the main technologies which has been utilized for this need is the Radio Frequency Identification (RFID) system. As a result of adopting this technology to the manufacturing industry environment, RFID Network Planning (RNP) has become a challenge. Mainly RNP deals with calculating the number and position of antennas which should be deployed in the RFID network to achieve full coverage of the tags that need to be read. The ultimate goal of this paper is to present and evaluate a way of modelling and optimizing nonlinear RNP problems utilizing artificial intelligence (AI) techniques. This effort has led the author to propose a novel AI algorithm, which has been named “hybrid AI optimization technique,” to perform optimization of RNP as a hard learning problem. The proposed algorithm is composed of two different optimization algorithms: Redundant Antenna Elimination (RAE) and Ring Probabilistic Logic Neural Networks (RPLNN). The proposed hybrid paradigm has been explored using a flexible manufacturing system (FMS), and results have been compared with Genetic Algorithm (GA) that demonstrates the feasibility of the proposed architecture successfully. PubDate: Wed, 14 Jun 2017 00:00:00 +000

Abstract: This paper addresses the problem of sliding mode control (SMC) design for a class of uncertain switched descriptor systems with state delay and nonlinear input. An integral sliding function is designed and an adaptive sliding mode controller for the reaching motion is then synthesised such that the trajectories of the resulting closed-loop system can be driven onto a prescribed sliding surface and maintained there for all subsequent times. Moreover, based on a new Lyapunov-Krasovskii functional, a delay-dependent sufficient condition is established such that the admissibility as well as the performance requirement of the sliding mode dynamics can be guaranteed in the presence of time delay, external disturbances, and nonlinear input which comprises dead-zones and/or sector nonlinearities. The major contributions of this paper of this approach include (i) the closed-loop system exhibiting strong robustness against nonlinear dynamics and (ii) the control scheme enjoying the chattering-free characteristic. Finally, two representative examples are given to illustrate the theoretical developments. PubDate: Tue, 13 Jun 2017 07:21:39 +000

Abstract: This paper studies internal reference price effects when competitive firms face reference price effects and make decisions based on partial information, where their decision-making mechanism is modeled by a dynamic adjustment process. It is shown that the evolution of this dynamic adjustment goes to stabilization if both adjustment speeds are small and the complexity of this evolution increases in adjustment speeds. It is proved that the necessary condition for flip bifurcation or Neimark-Sacker bifurcation will occur with the increase of adjustment speed in two special cases. What is more, numerical simulations show that these bifurcations do occur. Then, the impacts of parameters on stability and profits are investigated and some management insights for firms with limited information to take advantage of reference price effects are provided. PubDate: Tue, 13 Jun 2017 07:09:43 +000

Abstract: The pinning synchronous problem for complex networks with interval delays is studied in this paper. First, by using an inequality which is introduced from Newton-Leibniz formula, a new synchronization criterion is derived. Second, combining Finsler’s Lemma with homogenous matrix, convergent linear matrix inequality (LMI) relaxations for synchronization analysis are proposed with matrix-valued coefficients. Third, a new variable subintervals method is applied to expand the obtained results. Different from previous results, the interval delays are divided into some subdelays, which can introduce more free weighting matrices. Fourth, the results are shown as LMI, which can be easily analyzed or tested. Finally, the stability of the networks is proved via Lyapunov’s stability theorem, and the simulation of the trajectory claims the practicality of the proposed pinning control. PubDate: Thu, 08 Jun 2017 00:00:00 +000

Abstract: Autapse connection is considered on a biological neuron coupled by astrocyte, and the effect of autapse driving-induced response in electrical activities is investigated. In this paper, a simple network is developed on the Hodgkin-Huxley (HH) neuron coupled by astrocyte and the autapse effect is also considered. The modulation of autapse connected to HH neuron can change the membrane potential by applying time-delayed feedback along a close loop. It is found that the self-adaption of autapse driving can make the network of neuron-astrocyte generate different modes of electrical activities, and oscillating behavior of Ca2+ and IP3 setting is controlled. This new network model can give potential understanding about self-adaption of neuron to external forcing when the coupling of astrocyte and autapse is considered. PubDate: Mon, 05 Jun 2017 06:35:45 +000

Abstract: Conditions for the existence and representations of -, -, and -inverses which satisfy certain conditions on ranges and/or null spaces are introduced. These representations are applicable to complex matrices and involve solutions of certain matrix equations. Algorithms arising from the introduced representations are developed. Particularly, these algorithms can be used to compute the Moore-Penrose inverse, the Drazin inverse, and the usual matrix inverse. The implementation of introduced algorithms is defined on the set of real matrices and it is based on the Simulink implementation of GNN models for solving the involved matrix equations. In this way, we develop computational procedures which generate various classes of inner and outer generalized inverses on the basis of resolving certain matrix equations. As a consequence, some new relationships between the problem of solving matrix equations and the problem of numerical computation of generalized inverses are established. Theoretical results are applicable to complex matrices and the developed algorithms are applicable to both the time-varying and time-invariant real matrices. PubDate: Mon, 05 Jun 2017 00:00:00 +000

Abstract: The finite-time consensus problem in the networks of multiple mobile agents is comprehensively investigated. In order to resolve this problem, a novel nonlinear information exchange protocol is proposed. The proposed protocol ensures that the states of the agents are converged to a weighted-average consensus in finite time if the communication topology is a weighted directed graph with a spanning tree and each strongly connected component is detail-balanced. Furthermore, the proposed protocol is also able to solve the finite-time consensus problem of networks with a switching topology. Finally, computer simulations are presented to demonstrate and validate the effectiveness of the theoretical analysis under the proposed protocol. PubDate: Thu, 01 Jun 2017 07:13:55 +000

Abstract: We introduce and investigate a four-dimensional hidden hyperchaotic system without equilibria, which is obtained by augmenting the three-dimensional self-exciting homopolar disc dynamo due to Moffatt with an additional control variable. Synchronization of two such coupled disc dynamo models is investigated by active control and sliding mode control methods. Numerical integrations show that sliding mode control provides a better synchronization in time but causes chattering. The solution is obtained by switching to active control when the synchronization errors become very small. In addition, the electronic circuit of the four-dimensional hyperchaotic system has been realized in ORCAD-PSpice and on the oscilloscope by amplitude values, verifying the results from the numerical experiments. PubDate: Wed, 31 May 2017 00:00:00 +000

Abstract: There are many recent investigations on chaotic hidden attractors although hyperchaotic hidden attractor systems and their relationships have been less investigated. In this paper, we introduce a hyperchaotic system which can change between hidden attractor and self-excited attractor depending on the values of parameters. Dynamic properties of these systems are investigated. Fractional order models of these systems are derived and their bifurcation with fractional orders is discussed. Field programmable gate array (FPGA) implementations of the systems with their power and resource utilization are presented. PubDate: Tue, 30 May 2017 00:00:00 +000

Abstract: Self-organizing traffic lights have shown considerable improvements compared to traditional methods in computer simulations. Self-organizing methods, however, use sophisticated sensors, increasing their cost and limiting their deployment. We propose a novel approach using simple sensors to achieve self-organizing traffic light coordination. The proposed approach involves placing a computer and a presence sensor at the beginning of each block; each such sensor detects a single vehicle. Each computer builds a virtual environment simulating vehicle movement to predict arrivals and departures at the downstream intersection. At each intersection, a computer receives information across a data network from the computers of the neighboring blocks and runs a self-organizing method to control traffic lights. Our simulations showed a superior performance for our approach compared with a traditional method (a green wave) and a similar performance (close to optimal) compared with a self-organizing method using sophisticated sensors but at a lower cost. Moreover, the developed sensing approach exhibited greater robustness against sensor failures. PubDate: Tue, 30 May 2017 00:00:00 +000

Abstract: From economic inequality and species diversity to power laws and the analysis of multiple trends and trajectories, diversity within systems is a major issue for science. Part of the challenge is measuring it. Shannon entropy has been used to rethink diversity within probability distributions, based on the notion of information. However, there are two major limitations to Shannon’s approach. First, it cannot be used to compare diversity distributions that have different levels of scale. Second, it cannot be used to compare parts of diversity distributions to the whole. To address these limitations, we introduce a renormalization of probability distributions based on the notion of case-based entropy as a function of the cumulative probability . Given a probability density , measures the diversity of the distribution up to a cumulative probability of , by computing the length or support of an equivalent uniform distribution that has the same Shannon information as the conditional distribution of up to cumulative probability . We illustrate the utility of our approach by renormalizing and comparing three well-known energy distributions in physics, namely, the Maxwell-Boltzmann, Bose-Einstein, and Fermi-Dirac distributions for energy of subatomic particles. The comparison shows that is a vast improvement over as it provides a scale-free comparison of these diversity distributions and also allows for a comparison between parts of these diversity distributions. PubDate: Wed, 24 May 2017 07:51:02 +000

Abstract: This paper focuses on a class of event-triggered discrete-time distributed consensus optimization algorithms, with a set of agents whose communication topology is depicted by a sequence of time-varying networks. The communication process is steered by independent trigger conditions observed by agents and is decentralized and just rests with each agent’s own state. At each time, each agent only has access to its privately local Lipschitz convex objective function. At the next time step, every agent updates its state by applying its own objective function and the information sent from its neighboring agents. Under the assumption that the network topology is uniformly strongly connected and weight-balanced, the novel event-triggered distributed subgradient algorithm is capable of steering the whole network of agents asymptotically converging to an optimal solution of the convex optimization problem. Finally, a simulation example is given to validate effectiveness of the introduced algorithm and demonstrate feasibility of the theoretical analysis. PubDate: Sun, 21 May 2017 00:00:00 +000

Abstract: Terminal guidance law against the maneuvering target is always the focal point. Most of the literatures focus on estimating the acceleration of target and time to go in guidance law, which are difficult to acquire. This paper presents a terminal guidance law based on receding horizon control strategy. The proposed guidance law adopts the basic framework of receding horizon control, and the guidance process is divided into several finite time horizons. Then, optimal control theory and target motion prediction model are used to derive guidance law for minimum time index function with continuous renewal of original conditions at the initial time of each horizon. Finally, guidance law performs repeated iteration until intercepting the target. The guidance law is of subprime optimal type, requiring less guidance information, and does not need to estimate the acceleration of target and time to go. Numerical simulation has verified that the proposed guidance law is more effective than traditional methods on constant and sinusoidal target with bounded acceleration. PubDate: Thu, 18 May 2017 00:00:00 +000

Abstract: This paper focuses on a three-dimensional portfolio balanced risk index model for large-scale infrastructure project risk evaluation as a hot topic of current research. Taking subjectivity utility and complex evaluation motivation into consideration, a method of combinational equilibrium evaluation is built using the index form to reflect whole loss changes of risk. For risk index evaluation and measurement issues, this paper first constructs a risk evaluation index system and three risk coefficients of single factor by questionnaire survey and fuzzy evaluation. Then we calculate the risk index of single factor, which arrives at the classification and combination risk index through AHP method. Finally, we verify the index validity by analysis of examples. With this research we expand the evaluation dimension and provide a new analytical tool for risk monitoring and warning. PubDate: Sun, 14 May 2017 00:00:00 +000

Abstract: In the real world, there are a number of optimization problems whose search space is restricted to take binary values; however, there are many continuous metaheuristics with good results in continuous search spaces. These algorithms must be adapted to solve binary problems. This paper surveys articles focused on the binarization of metaheuristics designed for continuous optimization. PubDate: Thu, 11 May 2017 00:00:00 +000

Abstract: Complex systems are a broad concept that comprises many disciplines, including engineering systems. Regardless of their particular behavior, complex systems share similar behaviors, such as synchronization. This paper presents different techniques for determining the source of coupling when a set of oscillators synchronize. It is possible to identify the location and time variations of the coupling by applying a combination of analytical techniques, namely, the source of synchronization. For this purpose, the analysis of experimental data from a complex mechanical system is presented. The experiment consisted in placing a 24-bladed rotor under an airflow. The vibratory motion of the blades was recorded with accelerometers, and the resulting information was analyzed with four techniques: correlation coefficients, Kuramoto parameter, cross-correlation functions, and the recurrence plot. The measurements clearly show the existence of frequencies due to the foreground components and the internal interaction between them due to the background components (coupling). PubDate: Thu, 04 May 2017 00:00:00 +000

Abstract: In this paper, we present and illustrate a frozen Jacobian multistep iterative method to solve systems of nonlinear equations associated with initial value problems (IVPs) and boundary value problems (BVPs). We have used Jacobi-Gauss-Lobatto collocation (J-GL-C) methods to discretize the IVPs and BVPs. Frozen Jacobian multistep iterative methods are computationally very efficient. They require only one inversion of the Jacobian in the form of LU-factorization. The LU factors can then be used repeatedly in the multistep part to solve other linear systems. The convergence order of the proposed iterative method is , where is the number of steps. The validity, accuracy, and efficiency of our proposed frozen Jacobian multistep iterative method is illustrated by solving fifteen IVPs and BVPs. It has been observed that, in all the test problems, with one exception in this paper, a single application of the proposed method is enough to obtain highly accurate numerical solutions. In addition, we present a comprehensive comparison of J-GL-C methods on a collection of test problems. PubDate: Wed, 03 May 2017 00:00:00 +000

Abstract: For sequentially monitoring and controlling average and variability of an online manufacturing process, and control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional and charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments. PubDate: Sun, 30 Apr 2017 13:30:55 +000

Abstract: This paper deals with a competitor-competitor-mutualist Lotka-Volterra model. A series of sufficient criteria guaranteeing the stability and the occurrence of Hopf bifurcation for the model are obtained. Several concrete formulae determine the properties of bifurcating periodic solutions by applying the normal form theory and the center manifold principle. Computer simulations are given to support the theoretical predictions. At last, biological meaning and a conclusion are presented. PubDate: Wed, 26 Apr 2017 00:00:00 +000

Abstract: Considering the impact that physical distance and other properties have on the change of opinions, this paper introduces an intension model of the Hegselmann-Krause (KH) model—heterogeneous interaction (HI) model. Based on the classical KH model, HI model designs new interaction rules and the interactive radius considering the impact of heterogeneous attributes, such as physical distance, individual conformity, and authority. The experiment results show that the opinion evolution of the HI model will be similar to the classic KH model when the interactive radius is above the particular threshold value (). Unlike the KH model, which leads to the polarization phenomenon; most agents reach a consensus in HI model when the confidence radius equals 0.2, and the interactive radius remains within regulatory limits (). The conclusions show that interactive radius affects public opinion evolution. HI model can explain more conscious opinion evolution in real life and has significance that effectively guides public opinion. PubDate: Mon, 24 Apr 2017 08:21:24 +000

Abstract: We investigate a specific smart grid system and its nonlinear properties. Lyapunov exponents are derived to prove the existence of chaos and bifurcation and bicoherence contours are investigated to show the parameter dependence and existence of quadratic nonlinearities, respectively. A fractional order model of the smart grid system (FOSG) is then derived and bifurcation of the FOSG system with variation in the commensurate fractional order of the system is investigated to show that largest Lyapunov exponent of the system exists in fractional order. Hence we proposed two different control methods to suppress the chaotic oscillations. In the first method we derive fractional order adaptive sliding mode control (FOASMC) algorithm to control chaotic oscillations and in the second method we used genetically optimized fractional order PID controllers (GAFOPID) for chaos control. Numerical simulations are conducted to show the effectiveness of the controllers and also to prove that GAFOPID controllers are more effective than FOASMC controllers for fractional order systems. The GAFOPID controllers are then realized in FPGA to show that the proposed methodology is hardware realizable. PubDate: Thu, 13 Apr 2017 00:00:00 +000

Abstract: Here, we develop new heuristic algorithm for solving TSP (Travelling Salesman Problem). In our proposed algorithm, the agent cannot estimate tour lengths but detect only a few neighbor sites. Under the circumstances, the agent occasionally ignores the NN method (choosing the nearest site from current site) and chooses the other site far from current site. It is dependent on relative distances between the nearest site and the other site. Our algorithm performs well in symmetric TSP and asymmetric TSP (time-dependent TSP) conditions compared with the NN algorithm using some TSP benchmark datasets from the TSPLIB. Here, symmetric TSP means common TSP, where costs between sites are symmetric and time-homogeneous. On the other hand, asymmetric TSP means TSP where costs between sites are time-inhomogeneous. Furthermore, the agent exhibits critical properties in some benchmark data. These results suggest that the agent performs adaptive travel using limited information. Our results might be applicable to nonclairvoyant optimization problems. PubDate: Tue, 11 Apr 2017 00:00:00 +000

Abstract: The goal of this study was to reveal the mechanism of the Chinese industry-university-research collaborative innovation (IURCI) and interactions between the elements in the system and find issues that exist in the collaborative innovation process. Based on the theoretical perspective of innovation and complexity science, we summarized the elements of the IURCI as innovation capability, research and development (R&D) configuration, and knowledge transfer and established a theoretical model to describe the evolution of the IURCI system. We used simulation technology to determine the interactions among variables and the evolution trend of the system. The results showed that the R&D configuration can promote the evolution of innovation capability and knowledge transfer and that innovation capacity is the current dominant factor in the evolution of the Chinese IURCI system and is highly positively correlated with R&D configuration. The evolutionary trend of knowledge transfer was gentler, and its contribution to the evolution of the Chinese IURCI system was less than that of R&D configuration. When innovation, R&D configuration, and knowledge transfer are relatively balanced, the collaborative innovation system can achieve high speed and stable evolution. PubDate: Sun, 09 Apr 2017 00:00:00 +000

Abstract: Differential expression plays an important role in cancer diagnosis and classification. In recent years, many methods have been used to identify differentially expressed genes. However, the recognition rate and reliability of gene selection still need to be improved. In this paper, a novel constrained method named robust nonnegative matrix factorization via joint graph Laplacian and discriminative information (GLD-RNMF) is proposed for identifying differentially expressed genes, in which manifold learning and the discriminative label information are incorporated into the traditional nonnegative matrix factorization model to train the objective matrix. Specifically, -norm minimization is enforced on both the error function and the regularization term which is robust to outliers and noise in gene data. Furthermore, the multiplicative update rules and the details of convergence proof are shown for the new model. The experimental results on two publicly available cancer datasets demonstrate that GLD-RNMF is an effective method for identifying differentially expressed genes. PubDate: Thu, 06 Apr 2017 00:00:00 +000

Abstract: Optimizing average path length (APL) by adding shortcut edges has been widely discussed in connection with social networks, but the relationship between network diameter and APL is generally ignored in the dynamic optimization of APL. In this paper, we analyze this relationship and transform the problem of optimizing APL into the problem of decreasing diameter to 2. We propose a mathematic model based on a memetic algorithm. Experimental results show that our algorithm can efficiently solve this problem as well as optimize APL. PubDate: Tue, 28 Mar 2017 00:00:00 +000