By the use of the properties of ergodicity, a chaos algorithm for multilayer feedforward neural network is proposed with combining logistic mapping and BP algorithm. This algorithm can make BP algorithm to skip the lo...
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By the use of the properties of ergodicity, a chaos algorithm for multilayer feedforward neural network is proposed with combining logistic mapping and BP algorithm. This algorithm can make BP algorithm to skip the local minimum and can find the global minimum finally.
In multiobjective control of linear systems, critical issues are the size of optimization constraints, number of variables, and computation time. This paper investigates in a quantitative way, how some prominent metho...
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In multiobjective control of linear systems, critical issues are the size of optimization constraints, number of variables, and computation time. This paper investigates in a quantitative way, how some prominent methods from the literature behave with respect to these critical factors for different problem sizes. Moreover, an alternative formulation is proposed and compared to the existing approaches, showing its favorable properties for a selection of typical applications.
Chaotic neural networks have been applied to solve function optimization problems successfully. To improve the optimization capacity of the chaotic neural network, a new chaotic neural network model called wavelet cha...
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Chaotic neural networks have been applied to solve function optimization problems successfully. To improve the optimization capacity of the chaotic neural network, a new chaotic neural network model called wavelet chaotic neural network was presented by transferring sigmoid function to wavelet function. The reversed bifurcation figures of signal neural unit were given and the parameters of the new model were discussed. The wavelet function is a non-monotonic function, so the new model can spend the less time than the common chaotic neural network model in function optimization. The simulation result shows that the new chaotic neural network model is superior to the common neural network model
Chaotic neural networks have been applied to solve function optimization problems successfully. To improve the optimization capacity of the chaotic neural network, a new chaotic neural network model called wavelet cha...
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This paper deals with the robust stabilization for a class of linear Delta-operator formulated uncertain systems with state delays and jumping parameters. The transition of the jumping parameters in systems is governe...
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This paper deals with the robust stabilization for a class of linear Delta-operator formulated uncertain systems with state delays and jumping parameters. The transition of the jumping parameters in systems is governed by a finite-state Markov process. The class of systems is a hybrid class of systems with two components in the vector state. The first component refers to the mode and the second one to the state. The mode is described by a continuous Markov process with finite state space. The state in each mode is denoted by a stochastic differential equation. Based on stability theory in stochastic differential equations, a sufficient condition on the existence of robust stabilizing control law is derived. Based on this condition, a robust memoryless stabilizing control law is designed in terms of a set of linear matrix inequalities. A numerical example demonstrates the effect of the proposed design approach
This paper deals with the robust stabilizability and H infin disturbance attenuation for a class of uncertain descriptor time-delay systems with jumping parameters. The transition of the jumping parameters in the sys...
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This paper deals with the robust stabilizability and H infin disturbance attenuation for a class of uncertain descriptor time-delay systems with jumping parameters. The transition of the jumping parameters in the systems is governed by a finite-state Markov process. A sufficient condition on robust stabilizability is established based on the stability theory for stochastic differential equations. With the help of a set of coupled linear matrix inequalities, robust H infin controllers are designed to stochastically stabilize the given systems with a prescribed disturbance attenuation level represented by an H infin norm bound constraint. A numerical example demonstrates the effect of the proposed design approach
In this paper, the analysis techniques for decoupling of the aircraft motions are utilized to develop vehicle lateral control with advanced mode. Vehicle lateral dynamic is determined to have the steering input and co...
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In this paper, the analysis techniques for decoupling of the aircraft motions are utilized to develop vehicle lateral control with advanced mode. Vehicle lateral dynamic is determined to have the steering input and control torque input. The additional vehicle modes are also defined to using CCV concept. We use right eigenstructure assignment techniques and command generator tracker to design a control law for an lateral vehicle dynamics. The desired eigenvectors are chosen to achieve the desired decoupling (i.e., lateral direction speed and yaw late). The command generator tracker is used to ensure steady-state tracking of the driver's command. Finally, the developed design is utilized by using the lateral vehicle dynamic with four wheels.
In this paper, a modified version of the Chaos Shift Keying (CSK) scheme for secure encryption and decryption of data is proposed. The proposed scheme uses the effect of anti-synchronization, rather than synchronizati...
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In this paper, a modified version of the Chaos Shift Keying (CSK) scheme for secure encryption and decryption of data is proposed. The proposed scheme uses the effect of anti-synchronization, rather than synchronization. More specifically, the classical CSK method determines the correct value of binary signal through checking which unsynchronized system is getting synchronized. On the contrary, our novel method determines wrong value of binary signal through checking which already synchronized system is loosing synchronization. The advantage of the proposed method is two-fold. First, it requires very reasonable amount of data to encrypt and time to decrypt a single bit. Secondly, its security can be investigated and estimated as practically unbreakable. The main reason for both advantages is that anti-synchronization is thousand times faster than synchronization, even when using two close each to other chaotic systems. Our method is implemented and thoroughly tested on the recently introduced generalized Lorenz system (GLS) family making advantage of its special parametrization.
In this paper, the problem of simultaneously estimating the states and selected parameters of a diesel combustion engine air path is considered. Due to the high complexity of the nonlinear air path model, this problem...
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In this paper, the problem of simultaneously estimating the states and selected parameters of a diesel combustion engine air path is considered. Due to the high complexity of the nonlinear air path model, this problem is challenging, further complicated as decisive physical insight is available in the form of inequality constraints. We therefore provide a derivative-free estimation algorithm, combining constrained online optimization with an unscented Kalman filter covariance update formula. To keep generality, the algorithm is derived and examined for a general class of nonlinear constrained systems. Experimental results put to test, confirm the efficiency of the provided estimation algorithm.
The tracking problem for continuous-time systems with multiple input delays is investigated. Via Krein space method, the problem with multiple input delays is dual to that of fixed-lag smoothing for a stochastic model...
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The tracking problem for continuous-time systems with multiple input delays is investigated. Via Krein space method, the problem with multiple input delays is dual to that of fixed-lag smoothing for a stochastic model without delay. The optimal tracking controller is then designed by computing the gain matrices of the fixed-lag smoothing
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