System Identification Using Off-Optimum Data From A Genetic Algorithm: A Source Of Training Data For an Artificial Neural Network
نویسنده
چکیده
When developing an artificial neural net model of a system, the most efficient way to obtain training and test data is often to generate a large set of random inputs and run them through the model. But that is not the only way to do it. We demonstrate the use of genetic algorithm-generated data as a source of input-output pairs for training an artificial neural network. If the genetic algorithm and neural network are being developed together – for example, to provide system identification in support of a control system – this data is readily available and performs as well as a random search of the state space.
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