Learn More
Recurrent Neural Networks (RNN) have been developed for a better understanding and analysis of open dynamical systems. Still the question often arises if RNN are able to map every open dynamical system, which would be desirable for a broad spectrum of applications. In this article we give a proof for the universal approximation ability of RNN in state space(More)
In this paper we define and address the problem of safe exploration in the context of reinforcement learning. Our notion of safety is concerned with states or transitions that can lead to damage and thus must be avoided. We introduce the concepts of a safety function for determining a state's safety degree and that of a backup policy that is able to lead(More)
In this paper a new neural network based approach to control a gas turbine for stable operation on high load is presented. A combination of recurrent neural networks (RNN) and reinforcement learning (RL) is used. The authors start by applying an RNN to identify the minimal state space of a gas turbine's dynamics. Based on this the optimal control policy is(More)
This paper presents our Recurrent Control Neural Network (RCNN), which is a model-based approach for a data-efficient modelling and control of reinforcement learning problems in discrete time. Its architecture is based on a recurrent neural network (RNN), which is extended by an additional control network. The latter has the particular task to learn the(More)
In this work, we present a new model for a Recurrent Support Vector Machine. We call it intrinsic because the complete recurrence is directly incorporated within the considered optimisation problem. This approach offers the advantage that the model straightforwardly develops an algorithmic solution. We test the algorithm on several simple time series. The(More)
Statistical relational learning analyzes the probabilistic constraints between the entities, their attributes and relationships. It represents an area of growing interest in modern data mining. Many leading researches are proposed with promising results. However, there is no easily applicable recipe of how to turn a relational domain (e.g. a database) into(More)