WSEAS TRANSACTIONS on SYSTEMS
WSEAS TRANSACTIONS on SYSTEMS
Applying a genetic algorithms programming to natural cheese products
ACS'08 Proceedings of the 8th conference on Applied computer scince
A genetic algorithms programming application in natural cheese products
WSEAS TRANSACTIONS on SYSTEMS
Parameter identification of bilinear system based on genetic algorithm
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Design of a switching PID controller using advanced genetic algorithm for a nonlinear system
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Identification of a motor with multiple nonlinearities by improved genetic algorithm
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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In this paper, a new approach to nonlinear system identification using evolutionary LMS Algorithm has been proposed. System in our method consists of a static nonlinear function in series with a dynamic linear transfer function, which literature refers to them as Hammersteinmodels. Identified nonlinear function can be one of the hyperbolic function or a general format of (ax+b) or a combination of them. Genetic Algorithm is responsible for finding the correct structure and parameters of the nonlinear function, and number of zeros and poles of the linear transfer function as well. In order to speed up the convergence process, we use a kind of dynamic mutation rate that increase with respect to the generation passed while the fitness remains unchanged. As the linear identification algorithm we prefer to parameterize the problem as ARMA and use the traditional LMS algorithm. AIC is the fitness function evaluator of the GA chromosomes, using both total error and estimated order of the linear section. Two different simulations show the effectiveness of our method. In the simulation two hard nonlinear functions, saturation and deadzone, has been used and show that despite of small amount of information which is limited to input-output signals, our approach can considerably identify the systems.