Development of omni-directional correlation functions for nonlinear model validation
Automatica (Journal of IFAC)
Automatic State-Based Test Generation Using Genetic Algorithms
SYNASC '07 Proceedings of the Ninth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
A set of novel correlation tests for nonlinear system variables
International Journal of Systems Science
A correlation-test-based validation procedure for identified neural networks
IEEE Transactions on Neural Networks
On realizability of neural networks-based input-output models in the classical state-space form
Automatica (Journal of IFAC)
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Genetic algorithm is used in the present paper to perform evolutionary design of the closed loop control for the given process. Main distinctive feature of the proposed approach is that arguments of the fitness function describe model, and therefore controller quality, both in the open and closed loops. Namely model validity with cross-correlation functions determined in the open loop and mean square error is measured for the performance in the closed loop with a controller, which equations analytically derived from from the equations of the model.