The ComBack method is a memory reduction technique for explicit state space search algorithms. It enhances hash compaction with state reconstruction to resolve hash conflicts on-the-fly thereby ensuring full coverage of the state space. In this paper we provide two means to lower the run-time penalty induced by state reconstructions: a set of strategies to implement the caching method proposed ...

We generalize the multi step prediction cost function for an unbiased reconstruction of the dynamics for noisy time series recently proposed for the case of scalar time series (which requires a reconstruction in delay space) to the case of multivariate time series data. 1 Given a time series with irregular time dependence the reconstruction of the underlying deterministic dynamics ooers probabl...

OF DISSERTATION AN ECHO STATE MODEL OF NON-MARKOVIAN REINFORCEMENT LEARNING There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to doma...

A new model for tracking of feature points in dynamic image is proposed. The model is represented in a form of nonlinear state space model having state variables with positions of feature points, velocities for each object, and object labels that specify the associations between the feature points and the objects. We use particle filters with RaoBlackwellization to estimate the state of the non...

This paper presents a rigorous method for reconstructing events in digital systems. It is based on the idea, that once the system is described as a finite state machine, its state space can be explored to determine all possible scenarios of the incident. To formalize evidence, the evidential statement notation is introduced. It represents the facts conveyed by the evidence as a series of witnes...

in this article, the discrete time state space model with first-order autoregressive dependent process noise is considered and the recursive method for filtering, prediction and smoothing of the hidden state from the noisy observation is designed. the explicit solution is obtained for the hidden state estimation problem. finally, in a simulation study, the performance of the designed method ...

[1] The reconstruction of low-order nonlinear dynamics from the time series of a state variable has been an active area of research in the last decade. The 154 year long, biweekly time series of the Great Salt Lake volume has been analyzed by many researchers from this perspective. In this study, we present the application of a powerful state space reconstruction methodology using the method of...

steering assist system controls the force transfer behavior of the steering system and improves the steering probability of the vehicle. moreover, it is an interface between the diver and vehicle. fault detection in electrical assisted steering systems is a challenging problem due to frequently use of these systems. this paper addresses the fault detection and reconstruction in automotive elect...

Chaos control may be understood as the use of tiny perturbations for the stabilization of unstable periodic orbits embedded in a chaotic attractor. Since chaos may occur in many natural processes, the idea that chaotic behavior may be controlled by small perturbations of some physical parameter allows this kind of behavior to be desirable in different applications. In general, it is not necessa...

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and least squares support vector machine (LSSVM). First, the model confirms embedding dimension and delay time of the traffic flow, occupancy and...