29 research outputs found

    A model-based scheme for anticontrol of some chaotic systems

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    We consider a model-based approach for the anticontrol of some continuous time systems. We assume the existence of a chaotic model in an appropriate form. By using a suitable input, we match the dynamics of the controlled system and the chaotic model. We show that controllable systems can be chaotifled with the proposed method. We give a procedure to generate such chaotic models. We also apply an observer-based synchronization scheme to compute the required input

    A model-based scheme for anticontrol of some discrete-time chaotic systems

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    We consider a model-based approach for the anticontrol of some discrete-time systems. We first assume the existence of a chaotic model in an appropriate form. Then by using an appropriate control input we try to match the controlled system with the chaotic system model. We also give a procedure to generate the model chaotic systems in arbitrary dimensions. We show that with this approach, controllable systems can always be chaotified. Moreover, if the system to be controlled is stable, control input can be chosen arbitrarily small

    Parameter switching in a generalized Duffing system: Finding the stable attractors

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    This paper presents a simple periodic parameter-switching method which can find any stable limit cycle that can be numerically approximated in a generalized Duffing system. In this method, the initial value problem of the system is numerically integrated and the control parameter is switched periodically within a chosen set of parameter values. The resulted attractor matches with the attractor obtained by using the average of the switched values. The accurate match is verified by phase plots and Hausdorff distance measure in extensive simulations

    Neural Network Model for Apparent Deterministic Chaos in Spontaneously Bursting Hippocampal Slices

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    A neural network model that exhibits stochastic population bursting is studied by simulation. First return maps of inter-burst intervals exhibit recurrent unstable periodic orbit (UPO)-like trajectories similar to those found in experiments on hippocampal slices. Applications of various control methods and surrogate analysis for UPO-detection also yield results similar to those of experiments. Our results question the interpretation of the experimental data as evidence for deterministic chaos and suggest caution in the use of UPO-based methods for detecting determinism in time-series data.Comment: 4 pages, 5 .eps figures (included), requires psfrag.sty (included

    Model Based Anticontrol of Discrete-Time Systems

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    We will consider a model-based approach for the anticontrol of some discrete-time systems. We first assume the existence of a chaotic model in an appropriate form. Then by using an appropriate control input we try to match the controlled system with the chaotic system model

    Model based anticontrol of chaos

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    We will consider model based anticontrol of chaotic systems. We consider both continuous and discrete time cases. We first assume that the systems to be controlled are linear and time invariant. Under controllability assumption, we transform these systems into some canonical forms. We assume the existence of chaotic systems which has similar forms. Then by using appropriate inputs, we match the dynamics of the systems to be controlled and the model chaotic systems. © 2003 IEEE

    A Systematic Methodology for Multi-Images Encryption and Decryption Based on Single Chaotic System and FPGA Embedded Implementation

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    A systematic methodology is developed for multi-images encryption and decryption and field programmable gate array (FPGA) embedded implementation by using single discrete time chaotic system. To overcome the traditional limitations that a chaotic system can only encrypt or decrypt one image, this paper initiates a new approach to design n-dimensional (n-D) discrete time chaotic controlled systems via some variables anticontrol, which can achieve multipath drive-response synchronization. To that end, the designed n-dimensional discrete time chaotic controlled systems are used for multi-images encryption and decryption. A generalized design principle and the corresponding implementation steps are also given. Based on the FPGA embedded hardware system working platform with XUP Virtex-II type, a chaotic secure communication system for three digital color images encryption and decryption by using a 7D discrete time chaotic system is designed, and the related system design and hardware implementation results are demonstrated, with the related mathematical problems analyzed

    Stochastic neural network model for spontaneous bursting in hippocampal slices

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    A biologically plausible, stochastic, neural network model that exhibits spontaneous transitions between a low-activity ͑normal͒ state and a high-activity ͑epileptic͒ state is studied by computer simulation. Brief excursions of the network to the high-activity state lead to spontaneous population bursting similar to the behavior observed in hippocampal slices bathed in a high-potassium medium. Although the variability of interburst intervals in this model is due to stochasticity, first return maps of successive interburst intervals show trajectories that resemble the behavior expected near unstable periodic orbits ͑UPOs͒ of systems exhibiting deterministic chaos. Simulations of the effects of the application of chaos control, periodic pacing, and anticontrol to the network model yield results that are qualitatively similar to those obtained in experiments on hippocampal slices. Estimation of the statistical significance of UPOs through surrogate data analysis also leads to results that resemble those of similar analysis of data obtained from slice experiments and human epileptic activity. These results suggest that spontaneous population bursting in hippocampal slices may be a manifestation of stochastic bistable dynamics, rather than of deterministic chaos. Our results also question the reliability of some of the recently proposed, UPO-based, statistical methods for detecting determinism and chaos in experimental time-series data

    Controlling neuronal spikes

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    We propose two control strategies for achieving desired firing patterns in a physiologically realistic model neuron. The techniques are powerful, efficient, and robust, and we have applied them successfully to obtain a range of targeted spiking behaviors. The methods complement each other: one involves the manipulation of only a parameter, the applied soma current, and the other involves the manipulation of only a state variable, the membrane potential. Both techniques have the advantage that they are not measurement-intensive nor do they involve much run-time computation, as knowledge of only the interspike interval is necessary to implement control
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