Model-free controller design for discrete-valued input systems based on autoencoder
| Autor: | Eiji Konaka |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Computer Science::Machine Learning
Artificial neural network Computer science Feature vector 020208 electrical & electronic engineering Supervised learning 02 engineering and technology Variance (accounting) Overfitting Autoencoder Control theory Control system 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Layer (object-oriented design) |
| Zdroj: | 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). |
| Popis: | Switching control is an effective control technique for control systems equipped with low-resolution actuators. The controller design problem for this class of control system can be formulated as the construction of a mapping between the observed outputs and the discrete inputs, that is, the construction of a switching surface. The mapping can be learned by neural network; however, the training result is sensitive to the initial weights, especially when a redundant structure of the network is selected. In this paper, a controller design method based on a neural network with autoencoder is discussed. An autoencoder learns the identity mapping at each layer. As a result, the output from each layer automatically encodes the feature vectors. The trained weight is used as a suitable initial weight for overall supervised learning. Numerical simulations show that the proposed method can reduce stochastic variance and avoid overfitting, especially for redundant neural controllers. |
| Databáze: | OpenAIRE |
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