Parameterized Pattern Generation via Regression in the Model Space of Echo State Networks

Dissertation an Echo State Model of Non-markovian Reinforcement Learning

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...

 Abstract: Packing rectangular shapes into a rectangular space is one of the most important discussions on Cutting & Packing problems (C;P) such as: cutting problem, bin-packing problem and distributor's pallet loading problem, etc. Assume a set of rectangular pieces with specific lengths, widths and utility values. Also assume a rectangular packing space with specific width and length. The obj...

Reservoir regularization stabilizes learning of Echo State Networks with output feedback

Output feedback is crucial for autonomous and parameterized pattern generation with reservoir networks. Read-out learning can lead to error amplification in these settings and therefore regularization is important for both generalization and reduction of error amplification. We show that regularization of the inner reservoir network mitigates parameter dependencies and boosts the task-specific ...

A Hybrid Meta-heuristic Approach to Cope with State Space Explosion in Model Checking Technique for Deadlock Freeness

Model checking is an automatic technique for software verification through which all reachable states are generated from an initial state to finding errors and desirable patterns. In the model checking approach, the behavior and structure of system should be modeled. Graph transformation system is a graphical formal modeling language to specify and model the system. However, modeling of large s...

Online State Space Model Parameter Estimation in Synchronous Machines

The purpose of this paper is to present a new approach based on the Least Squares Error method for estimating the unknown parameters of the nonlinear 3rd order synchronous generator model. The proposed method uses the mathematical relationships between the machine parameters and on-line input/output measurements to estimate the parameters of the nonlinear state space model. The field voltage is...