Chaotic Dynamics in Social Foraging Swarms—An Analysis

Das, Swagatam; Halder, Udit; Maity, Dipankar

doi:10.1109/tsmcb.2012.2186799

Cited by 19 publications

(11 citation statements)

References 40 publications

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“…Similarly in case of quadratic profile the trajectory is exponential. Whereas in the simulations of Das et al [43] we notice that there are no such linear or exponential variations. We can use this phenomenon as an optimizing tool; the velocity toward the optimum of the σ profile reveals the fact that these characteristics can be used to find the optimum of a function.…”

Section: Mean Swarm Trajectorymentioning

confidence: 56%

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Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Das

2015

Nonlinear Dyn

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This article investigates the chaotic dynamical characteristics of a discrete-time social foraging swarm model in which the individuals move in a ddimensional space according to an attractant/repellent or a nutrient profile. The collective behavior of the swarm model results from the balance between interindividual interactions and the simultaneous interactions of the swarm members with their environment. Analysis undertaken in the paper indicates that chaos can be introduced into the system by tuning the parameters of the mutual attraction-repulsion function and the attractant-repellent profiles of the swarm dynamics [as given in Eq. (1)]. Ranges of different parameters to ensure sufficient condition for chaotic characteristics have been determined. Apart from chaos, stable and limit cyclic behaviors are also demonstrated for various parameter ranges. Effect of different attractant/repellent gradient profiles on the mean swarm trajectory in the presence of chaos has also been studied. Presence of chaos is determined by means of the Lyapunov exponent. Results obtained from the theoretical analysis are validated through comprehensive simulations.

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Section: Mean Swarm Trajectorymentioning

confidence: 56%

“…In the previous work [43], where we did not consider any profile and the mean swarm position was a fixed point. This phenomenon demonstrates that though the swarm is showing a chaotic behavior, the mean swarm position is constant throughout the whole time.…”

Section: Mean Swarm Trajectorymentioning

confidence: 99%

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Das

2015

Nonlinear Dyn

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“…Ever since the classic effort by Pecora and Carroll [1], the synchronization in chaotic systems became an active research area because of its useful applications [2][3][4][5]. Different synchronization schemes for chaotic systems were studied and investigated [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification and Hybrid Synchronization in an Array of Coupled Chaotic Systems with Ring Connection: An Adaptive Integral Sliding Mode Approach

Siddique

Rehman

2018

Mathematical Problems in Engineering

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This article presents an adaptive integral sliding mode control (SMC) design method for parameter identification and hybrid synchronization of chaotic systems connected in ring topology. To employ the adaptive integral sliding mode control, the error system is transformed into a special structure containing nominal part and some unknown terms. The unknown terms are computed adaptively. Then the error system is stabilized using integral sliding mode control. The controller of the error system is created that contains both the nominal control and the compensator control. The adapted laws and compensator controller are derived using Lyapunov stability theory. The effectiveness of the proposed technique is validated through numerical examples.

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“…A lot of works have been given on this theme because of its possible application in many fields such as communications, information processing [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. For example, Liu et al [19] discussed robust synchronization of uncertain complex networks by using impulsive control.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network synchronization for uncertain strick-feedback chaotic systems subject to dead-zone input

2018

Adv Differ Equ

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In this paper, an adaptive neural network (NN) synchronization controller is designed for two identical strict-feedback chaotic systems (SFCSs) subject to dead-zone input. The dead-zone models together with the system uncertainties are approximated by NNs. The dynamic surface control (DSC) approach is applied in the synchronization controller design, and the traditional problem of "explosion of complexity" that usually occurs in the backstepping design can be avoided. The proposed synchronization method guarantees the synchronization errors tend to an arbitrarily small region. Finally, this paper presents two simulation examples to confirm the effectiveness and the robustness of the proposed control method.

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Chaotic Dynamics in Social Foraging Swarms—An Analysis

Cited by 19 publications

References 40 publications

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Chaotic patterns in the discrete-time dynamics of social foraging swarms with attractant–repellent profiles: an analysis

Parameter Identification and Hybrid Synchronization in an Array of Coupled Chaotic Systems with Ring Connection: An Adaptive Integral Sliding Mode Approach

Adaptive neural network synchronization for uncertain strick-feedback chaotic systems subject to dead-zone input

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