Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
An adaptive fuzzy observer-based approach for chaotic synchronization
International Journal of Approximate Reasoning
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, presented a new fast algorithm for optimization of observer gain matrix. In most of papers for nonlinear observer design in chaotic systems for synchronization problem used a mathematical procedure based on theorems and assumption. In this work far from any complex mathematical computation and use the benefits of a heuristic method for optimization the obtained gain matrix of Luenberger (that is one of most useful method in design of observer). For our purpose the Particle Swarm Optimization (PSO) algorithm is used. This work is on performance comparison with a model-based observer, State Dependent Riccati Equation (SDRE) is proposed. The performances of these observers are shown by using a Lorenz system (that is a well-known chaotic system). Both approaches is then applied to induced a method of secure communications, by chaotic masking, where the message is injected into a chaotic signal generated in the transmitter where they are simultaneously over noisy channel to the receiver. Simulation results show the effectiveness of the proposed scheme.