Tribal particle swarm optimization for neurofuzzy inference systems and its prediction applications

This study presents tribal particle swarm optimization (TPSO) to optimize the parameters of the functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed TPSO uses particle swarm optimization (PSO) as evolution strategies of the tribes optimization algorithm (TOA) to balance local and global exploration of the search space. The proposed TPSO uses a self-clustering algorithm to divide the particle swarm into multiple tribes, and selects suitable evolution strategies to update each particle. The TPSO also uses a tribal adaptation mechanism to remove and generate particles and reconstruct tribal links. The tribal adaptation mechanism can improve the qualities of the tribe and the tribe adaptation. Finally, the FLNIS model with the proposed TPSO (FLNIS-TPSO) was used in several predictive applications. Experimental results demonstrated that the proposed TPSO method converges quickly and yields a lower RMS error than other current methods.     

Analysis of predator-prey models with continuous threshold harvesting

We formulate an alternative form of threshold harvesting and investigate its properties within the framework of a predator-prey model. Our formulation accounts for economic constraints and is defined as a continuous harvesting function of the predator species. Theoretical and numerical analysis indicate that our formulation, which we call continuous threshold policy (CTP), can help improve undesirable behavior of the predator-prey ecosystem. Our study includes uniform boundedness of solutions, stability of equilibria and periodic orbits, bifurcations, and heteroclinic orbits.     

Density Delay and Organizational Survival: Computational Models and Empirical Comparisons

Research on the ecological dynamics oforganizational populations has demonstrated that competitiveconditions at the time of founding have enduring effects onorganizational survival. According to ecological theories,organizational life chances are systematically affected by density (the number of organizations in a population) at thetime of founding because the lower resource endowments thatcharacterize organizations appearing in periods of highpopulation density tend to become self-reinforcing, and—over time—amplify differences in mortality rates oforganizations founded under different conditions. However,credible arguments have been offered that could justify both positive and negative effects of the delayed effectsof population density on organizational mortality rates, andreceived empirical research in part reflects this ambiguity.To develop new insight into this issue and to explore theboundaries of received empirical results, in this study wepresent a computational model of organizational evolutionaccording to which the global dynamics of organizationalpopulations emerge from the iteration of simple rules oflocal interaction among individual organizations. We use the synthetic data produced by simulation to estimate eventhistory models of organizational mortality, and compare theparameter estimates with those reported in the most recentempirical studies of actual organizational populations. Theconclusions supported by the model qualify and extendreceived empirical results, and suggest that delayed effectsof density are highly sensitive the details of local structure of connections among members of organizationalpopulations.     

Large deviations and occupation times for spin particle systems with long range interactions

The large deviation principle for spin particle systems with long range interactions has been studied. It is shown that most of the results in Chen J. W. and Dai Pra P. ’s previous papers can be extended to the present situation. A particularly interesting result is the variational principle which characterizes the stationary Markov measures of such systems as the zeros of the governing LD rate functions. Uniqueness of such measure is studied from this as well as other point of view. We then apply the results to the occupation times of the systems. New large deviation and convergence results are obtained.     

Dynamic range in a neuron network with electrical and chemical synapses

The dynamic range is the logarithmic difference between maximum and minimum levels of sensation produced by known stimuli. In the human sensory systems the dynamic ranges are typically larger than for single neurons, this amplification being essentially a collective effect of the neural network. We investigated the dynamic range exhibited by a cellular automaton network with electrical and chemical synapses, when the stimuli are modelled by a Poisson process of suprathreshold events of stereotyped unit amplitude and the neuron response is its average firing rate.     

Simple and indecomposable twofold cyclic triple systems from Skolem sequences

We construct simple indecomposable twofold cyclic triple systems TS₂(v) for all v ≡ 0, 1, 3, 4, 7, and 9(mod 12), where v = 4 or v ≥ 12, using Skolem‐type sequences. © 2000 John Wiley & Sons, Inc. J Combin Designs 8: 402–410, 2000     

Approximate and efficient calculation of dominant Lyapunov exponents of high-dimensional nonlinear dynamic systems

This short communication presents an efficient method for calculating dominant Lyapunov exponents (LEs) of high-dimensional nonlinear dynamic systems based on their reduced-order models obtained from the linear model reduction theory. Mathematical derivation shows that the LEs of the reduced-order models correspond to the dominant LEs of the original systems. Two numerical examples are provided to demonstrate the effectiveness of the method.     

Large deviations from a macroscopic scaling limit for particle systems with Kac interaction and random potential

We consider a lattice gas in a periodic d-dimensional lattice of width γ⁻¹, γ>0, interacting via a Kac's type interaction, with range and strength γ^d, and under the influence of a random one body potential given by independent, bounded, random variables with translational invariant distribution. The system evolves through a conservative dynamics, i.e. particles jump to nearest neighbor empty sites, with rates satisfying detailed balance with respect to the equilibrium measures. In [M. Mourragui, E. Orlandi, E. Saada, Macroscopic evolution of particles systems with random field Kac interactions, Nonlinearity 16 (2003) 2123–2147] it has been shown that rescaling space as γ⁻¹ and time as γ⁻², in the limit γ→0, for dimensions d3, the macroscopic density profile ρ satisfies, a.s. with respect to the random field, a non-linear integral partial differential equation, having the diffusion matrix determined by the statistical properties of the external random field. Here we show an almost sure (with respect to the random field) large deviations principle for the empirical measures of such a process. The rate function, which depends on the statistical properties of the external random field, is lower semicontinuous and has compact level sets.     

Instability and refinement of models in long term planning of large scale projects

Instability of models used in long term planning of large scale industrial projects is demonstrated. The uncertainty band around the balance set is introduced to account for unpredictable variations of important parameters or utilities created by instability in multi-objective optimization of large scale projects. Then, instability of dynamic models of growth is considered including population dynamics with saturation and long term optimal planning in social and economic spheres. A method of successive refinement in a synthetic multi-model system is proposed, and application of the sequence of refined models is illustrated on a real-life example of construction of a dam with yearly refinements of the initial model, based on past history of project realization.     

Discretization of continuous systems with internal and external point delays through a quasiparametrical ARMA model

In this note, a discrete input/output model, which involves an ARMA part plus a nonrecursive additive term associated with the initial conditions of the free response, is formulated for continuous linear time-invariant systems involving internal and external point delays. The model is obtained from the application of the Cayley-Hamilton theorem to the continuous state-transition matrix. In some particular situations of asymptotic stability of the free system, the additive term associated with the response to initial conditions tends to a constant as time increases to infinity, and can be compensated through feedback so that the closed-loop model becomes a classical ARMA model     

Parameter identification and synchronization of fractional-order chaotic systems

The knowledge about parameters and order is very important for synchronization of fractional-order chaotic systems. In this article, identification of parameters and order of fractional-order chaotic systems is converted to an optimization problem. Particle swarm optimization algorithm is used to solve this optimization problem. Based on the above parameter identification, synchronization of the fractional-order Lorenz, Chen and a novel system (commensurate or incommensurate order) is derived using active control method. The new fractional-order chaotic system has four-scroll chaotic attractors. The existence and uniqueness of solutions for the new fractional-order system are also investigated theoretically. Simulation results signify the performance of the work.     

Critical exponents of Fujita type for certain evolution equations and systems with spatio-temporal fractional derivatives

This paper is concerned with establishing necessary or sufficient conditions for the existence of solutions to evolution equations with fractional derivatives in space and time. The Fujita exponent is determined. Then, these results are extended to systems of reaction-diffusion equations. Our new results shed lights on important practical questions.     

LDG method for reaction-diffusion dynamical systems with time delay

In this paper we introduce a local discontinuous Galerkin method to solve nonlinear reaction-diffusion dynamical systems with time delay. Stability and convergence of the schemes are obtained. Finally, numerical examples on two biologic models are shown to demonstrate the accuracy and stability of the method.     

Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models

Time series are found widely in engineering and science. We study forecasting of stochastic, dynamic systems based on observations from multivariate time series. We model the domain as a dynamic multiply sectioned Bayesian network (DMSBN) and populate the domain by a set of proprietary, cooperative agents. We propose an algorithm suite that allows the agents to perform one-step forecasts with distributed probabilistic inference. We show that as long as the DMSBN is structural time-invariant (possibly parametric time-variant), the forecast is exact and its time complexity is exponentially more efficient than using dynamic Bayesian networks (DBNs). In comparison with independent DBN-based agents, multiagent DMSBNs produce more accurate forecasts. The effectiveness of the framework is demonstrated through experiments on a supply chain testbed.     

Regular linear systems governed by systems with state, input and output delays

^** Email: hadd{at}ucam.ac.ma^*** Email: idrissi{at}ucam.ac.ma In this paper, we give a new reformulation of linear systemswith delays in input, state and output. We show that these systemscan be written as a regular linear system without delays. Thetechnique used here is essentially based on the theory recentlydeveloped by Salamon and Weiss and the shift in semigroup properties.Our framework can be applied, in particular, when the delayoperators are given by Riemann–Stieltjes integrals.     

An inexact secant algorithm for large scale nonlinear systems of equalities and inequalities

In this paper, an inexact secant algorithm in association with nonmonotone technique and filter is proposed for solving the large scale nonlinear systems of equalities and inequalities. The systems are transformed into a continuous constrained optimization solved by inexact secant algorithm. Global convergence of the proposed algorithm is established under the reasonable conditions. Numerical results validate the effectiveness of our approach.