نتایج جستجو برای: universal approximator
تعداد نتایج: 106435 فیلتر نتایج به سال:
The first step of the approach in [1] is a transformation which eliminates the negated variables in the monomials of the DNF representation and in the clauses of the CNF representation of the functions computed at the nodes of the given standard network resulting into the so-called reduced DNF and CNF representations of these functions. Theorem 5 characterizes the reduced DNF and CNF representa...
Transports on rail are increasing and major railway infrastructure investments are expected. An important part of this infrastructure is the railway power supply system. The future railway power demands are naturally not known for certain. This means investment planning for an uncertain future. The more remote the uncertain future, the greater the amount of scenarios that have to be considered....
We discuss the temporal di erence learning algorithm as applied to approximating the cost to go function of an in nite horizon discounted Markov chain The algorithm we analyze updates parameters of a linear function approximator on line during a single endless traject ory of an irreducible aperiodic Markov chain with a nite or in nite state space We present a proof of convergence with probabili...
Introduction: Laparoscopic suturing is a tedious procedure which has a long learning curve. Hence, we sought to develop an alternative simple and reliable technique for laparoscopic tissue approximation to enhance laparoscopic reconstructive procedures. In this video, we present our method for using newly developed, nonperforating titanium clips (U.S. Surgical Inc., Norwalk, CT) to perform a Ur...
Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution ...
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning tasks, TD methods require a function approximator to represent the value function. However, using function approximators requires manually making crucial representational decisions. This paper investigates evolutionary...
We discuss the temporal-di erence learning algorithm, as applied to approximating the cost-to-go function of an in nite-horizon discounted Markov chain. The algorithm we analyze updates parameters of a linear function approximator on{line, during a single endless trajectory of an irreducible aperiodic Markov chain with a nite or in nite state space. We present a proof of convergence (with proba...
Concept learning in robotics is an extremely challenging problem. Sensory data is often high-dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept lear...
This paper addresses an approximation-based anti-windup (AW) control strategy for suppressing the windup effect caused by actuator saturation nonlinearity in proportional–integral–derivative (PID) controlled systems. The effect of actuator constraint is firstly regarded as a disturbance imported to the PID controller. The external disturbance can then be modeled by a linear differential equatio...
since esp received universal attention to smooth the path for academic studies and productions, a great deal of research and studies have been directed towards this area. swales’ (1990) model of ra introduction move analysis has served a pioneering role of guiding many relevant studies and has proven to be productive in terms of helpful guidelines that are the outcome of voluminous productions ...
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