Learning to train neural networks for real-world control problems

Over the past three years, our group has concentrated on the application of neural network methods to the training of controllers for real-world systems. This presentation describes our approach, surveys what we have found to be important, mentions some contributions to the field, and shows some representative results. Topics discussed include: (1) executing model studies as rehearsal for experimental studies; (2) the importance of correct derivatives; (3) effective training with second-order (DEKF) methods; (4) the efficacy of time-lagged recurrent networks; (5) liberation from the tyranny of the control cycle using asynchronous truncated backpropagation through time; and (6) multistream training for robustness. Results from model studies of automotive idle speed control serve as examples for several of these topics

Learning Context Sensitive Languages with LSTM Trained with Kalman Filters

Unlike traditional recurrent neural networks, the long shortterm memory (LSTM) model generalizes well when presented with training sequences derived from regular and also simple nonregular languages

Online Symbolic-Sequence Prediction with Discrete-Time Recurrent Neural Networks

This paper studies the use of discrete-time recurrent neural networks for predicting the next symbol in a sequence. The focus is on online prediction, a task much harder than the classical offine grammatical inference with neural networks. The results obtained show that the performance of recurrent networks working online is acceptable when sequences come from finite-state machines or even from some chaotic sources. When predicting texts in human language, however, dynamics seem to be too complex to be correctly learned in real-time by the net

Improving Long-Term Online Prediction with Decoupled Extended Kalman Filters

Long short-term memory (LSTM) recurrent neural networks (RNNs) outperform traditional RNNs when dealing with sequences involving not only short-term but also long-term dependencies. The decoupled extended Kalman filter learning algorithm (DEKF) works well in online environments and reduces significantly the number of training steps when compared to the standard gradient-descent algorithms