Hello, I'm working currently with prediction-problems for dynamical systems, e.g. single pendulum with friction. At the moment I'm testing neural networks for time series predictions, although my knowledge is very basic. My understanding of neural networks in light of dynamical systems is that they are working like a flexible state-space-model. Training the neural network with some testdata should result in an accurate state-space-model, which can be used for predictions, am I right?
ANSWER
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data = training + validation + test design = training + validation nondesign = test nontraining = validation + test
TRAINING:
1. Given input matrix, target matrix and training parameters, estimate the weights and biases. 2. Performance estimate is biased because the same data is used for training and performance estimation.
1. Choose nonweight parameters (e.g., learning rate, momentum constant, stopping epoch...) 2. Rank multiple designs 3. Performance estimates slightly biased because the same data is used to choose parameters
TEST:
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