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Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Forecasting Series-Based Stock Price Data Using Direct Reinforcement Learning, H. Li, Cihan H. Dagli, David Lee Enke
Engineering Management and Systems Engineering Faculty Research & Creative Works
A significant amount of work has been done in the area of price series forecasting using soft computing techniques, most of which are based upon supervised learning. Unfortunately, there has been evidence that such models suffer from fundamental drawbacks. Given that the short-term performance of the financial forecasting architecture can be immediately measured, it is possible to integrate reinforcement learning into such applications. In this paper, we present the novel hybrid view for a financial series and critic adaptation stock price forecasting architecture using direct reinforcement. A new utility function called policies-matching ratio is also proposed. The need for the …
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.