Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Frequency-sampling filters: an improved model structure for step-response identification
Automatica (Journal of IFAC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Nonlinear Hammerstein Model Identification Using Genetic Algorithm
ICAIS '02 Proceedings of the 2002 IEEE International Conference on Artificial Intelligence Systems (ICAIS'02)
Probabilistic neural network classification for model β-Glucan suspensions
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Automatica (Journal of IFAC)
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Autonomic control of food extruders has attracted considerable in recent years. With limited understanding of the complex physio-chemical interactions during the food extrusion process, designing a control system for food extruder is not easy. The common approach is to determine the operating conditions and then to maintain these values as closely as possible using various control loops, if not manual control. This paper applies genetic algorithms to achieve the parameters of the twin-screw food extrusion process. The genetic algorithms are very suitable for searching discrete, noisy, multimodal and complex space. The sum of square error on magnitude and phase of the twin screw food extrusion process is minimize and receiving outstanding in shape the measured system extracted from the frequency response analysis of the food extrusion process. As recognized, exploitation of the optimization based on Genetic Algorithms gives advanced results.