An automatic monitoring and intervention algorithm that permits the supervision of very general aspects in an univariate linear gaussian state space model is proposed. The algorithm makes use of a model comparison and selection approach within a Bayesian framework. In addition, this algorithm incorporates the possibility of eliminating earlier interventions when subsequent evidence against them...

design process is one of the main processes practiced in engineering. in this paper, after demonstrating the place of engineering among the topics discussed about technology, design process is studied from the perspective of three different models. the first model, vincenti’s model, provides an abstract explanation of the design process; the second model, bucciarelli’s model, has a sociological...

There are various fractional order systems existing. This paper deals with the modelling of fractional order systems using an old and unique model structure i.e. state space model. The fractional order process system can be mathematically modelled by state space model. Simulation results validated that the fractional order model using state space is better as compared to other models such as fi...

In this paper, we consider formal series associated with events, profiles derived from events, and statistical models that make predictions about events. We prove theorems about realizations for these formal series using the language and tools of Hopf algebras.

Learning a dynamics model and a reward model during reinforcement learning is a useful way, since the agent can also update its value function by using the models. In this paper, we propose a general dynamics model that is a composition of the feature space dynamics model and the state space dynamics model. This way enables to obtain a good generalization from a small number of samples because ...

Abstract Deep state space models (SSMs) are an actively researched model class for temporal developed in the deep learning community which have a close connection to classic SSMs. The use of SSMs as black-box identification can describe wide range dynamics due flexibility neural networks. Additionally, probabilistic nature allows uncertainty system be modelled. In this work SSM and its paramete...