نتایج جستجو برای: universal approximator

تعداد نتایج: 106435  

2006
Peter Peshev Dimiter Skordev

Let us call an approximator of a complex number α any sequence γ0, γ1, γ2, . . . of rational complex numbers such that |γt − α| ≤ 1 t+ 1 , t = 0, 1, 2, . . . Denoting by N the set of the natural numbers, we shall call a representation of α any 6-tuple of functions f1, f2, f3, f4, f5, f6 from N into N such that the sequence γ0, γ1, γ2, . . . defined by γt = f1(t)− f2(t) f3(t) + 1 + f4(t)− f5(t) ...

2011
André da Motta Salles Barreto Doina Precup Joelle Pineau

Kernel-based reinforcement-learning (KBRL) is a method for learning a decision policy from a set of sample transitions which stands out for its strong theoretical guarantees. However, the size of the approximator grows with the number of transitions, which makes the approach impractical for large problems. In this paper we introduce a novel algorithm to improve the scalability of KBRL. We resor...

Journal: :IEEE Access 2023

The radial basis function (RBF) neural network is a type of universal approximator, and has been widely used in various fields. Improving the training speed compactness RBF networks are critical for promoting their applications. In present study, we propose simple, fast, effective method, which based on residual extreme points neighborhoods (thus called REN method short this paper). calculates ...

Journal: :IEEE Trans. Systems, Man, and Cybernetics, Part C 2000
Goran S. Dordevic Milan Rasic Dragan Kostic Veljko Potkonjak

Development of skilled robotics draws clues from model-based theories of human motor control. Thus, a comprehensive anthropomorphic background is given in the introductory part of the paper. Skills in robotics are viewed as a tool for fast and efficient real-time control that can handle complexity and nonlinearity of robots, generally aiming at robot autonomy. Particularly, a skill of redundanc...

1992
Steven J. Bradtke

Recent research on reinforcement learning has focused on algorithms based on the principles of Dynamic Programming (DP). One of the most promising areas of application for these algorithms is the control of dynamical systems, and some impressive results have been achieved. However, there are significant gaps between practice and theory. In particular, there are no con vergence proofs for proble...

2007
Lisa Bonnici

Since the 1980s, the functions of non-traditional LIKE have been extensively examined (Romaine and Lange, 1991; Dailey O’Cain, 2000; Fuller, 2003; D’Arcy, 2005). Functions of LIKE have most commonly been positioned in two subgroups—a discourse introducing or quotative LIKE (Romaine and Lange, 1991), and a discourse (also, pragmatic, focuser) LIKE (Underhill, 1988; Andersen, 2001; D’Arcy, 2005)....

Journal: :Information Sciences 2022

In this paper, a Q-learning algorithm is proposed to solve the linear quadratic regulator problem of black box systems. The only has access input and output measurements. A Luenberger observer parametrization constructed using control new obtained from factorization utility function. An integral reinforcement learning approach used develop approximator structure. gradient descent update rule es...

Journal: :Applied Intelligence 2022

Navigating mobile robots along time-efficient and collision-free paths in crowds is still an open challenging problem. The key to build a profound understanding of the crowd for robots, which basis proactive foresighted policy. However, since interaction mechanisms among pedestrians are complex sophisticated, it difficult describe model them accurately. For excellent approximation capability de...

2011
Benjamin Yackley Terran Lane

Existing methods to search for an optimum Bayesian network su er when the size of the data set grows to be too large. The number of possible networks grows superexponentially in the number of variables, and it becomes increasingly time-consuming to get reasonable results; in fact, nding an exact optimal network for a given data set is an NP-complete problem, so the question is often to nd a net...

Journal: :FO & DM 2012
Osonde Osoba Sanya Mitaim Bart Kosko

We prove that three independent fuzzy systems can uniformly approximate Bayesian posterior probability density functions by approximating the prior and likelihood probability densities as well as the hyperprior probability densities that underly the priors. This triply fuzzy function approximation extends the recent theorem for uniformly approximating the posterior density by approximating just...

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