Neuroplanners for hand/eye coordination

Gräf,; LaLonde,

doi:10.1109/ijcnn.1989.118296

Cited by 18 publications

(2 citation statements)

References 6 publications

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“…Additionally, they also tend to learn their places topologically. This feature is particularly important for problems which involve two and three-dimensional physical spaces, and is indeed, the principal motivation for the SOM being used in path planning and obstacle avoidance in Robotics 19,20,38,52,53,54].…”

Section: Introductionmentioning

confidence: 99%

Discrete vector quantization for arbitrary distance function estimation

Oommen

Altınel²,

Aras³

1998

IEEE Trans. Syst., Man, Cybern. B

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There are currently many vastly different areas of research involving adaptive learning. Among them are the two areas that concern neural networks and learning automata. This paper develops a method by which the general philosophies of vector quantization (VQ) and discretized automata learning can be incorporated for the computation of arbitrary distance functions. The latter is a problem which has important applications in logistics and location analysis. The input to our problem is the set of coordinates of a large number of nodes whose internode arbitrary "distances" have to be estimated. To render the problem interesting, nontrivial, and realistic, we assume that the explicit form of this distance function is both unknown and uncomputable. Unlike traditional operations research methods, which use optimized parametric functional estimators, we have utilized discretized VQ principles to first adaptively polarize the nodes into subregions. Subsequently, the parameters characterizing the subregions are learned by using a variety of methods (including, for academic purposes, a VQ strategy in the meta-domain). After an initial training phase, a system which achieves distance estimation attempts to yield an estimate of any node-pair distance without actually deriving an explicit form for the unknown function. The algorithms have been rigorously tested for the actual road-travel distances involving cities in Turkey and the results obtained are conclusive. Indeed, these present results are the best currently available from any single or hybrid strategy.

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Section: Introductionmentioning

confidence: 99%

Discrete vector quantization for arbitrary distance function estimation

Oommen

Altınel²,

Aras³

1998

IEEE Trans. Syst., Man, Cybern. B

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show abstract

“…Additionally, they also tend to learn their Partially supported by the Turkish Scienti c and Technical Research Council (T UB _ ITAK) Grant TBAG-1336 y Partially supported by the National Sciences and Engineering Research Council (NSERC) of Canada places topologically. This feature is particularly important for problems which involve two and threedimensional physical spaces, and is indeed, the principal motivation for the SOM being used in path planning and obstacle avoidance in Robotics 18,19,35,42,43,44] . In this paper, we shall use the principles of the SOM (or more precisely, the principles of Vector Quantization (VQ)) in the estimation of arbitrary distance functions { a problem which has received much attention in Logistics and Location Analysis 16,31].…”

Section: Introductionmentioning

confidence: 99%

Vector Quantization for Arbitrary Distance Function Estimation

Altınel¹,

Oommen

Aras

1997

INFORMS Journal on Computing

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In this paper we apply the concepts of Vector Quantization (VQ) for the determination of arbitrary distance functions { a problem which has important applications in Logistics and Location Analysis. The input to our problem is the set of coordinates of a large number of nodes whose inter-node arbitrary \distances" have to be estimated. To render the problem interesting, non-trivial and realistic, we assume that the explicit form of this distance function is both unknown and uncomputable. Unlike traditional Operations Research methods, which compute aggregate parameters of functional estimators according to certain goodness-of-t criteria, we have utilized VQ principles to rst adaptively polarize the nodes into sub-regions. Subsequently, the parameters characterizing the sub-regions are learnt by using a variety of methods (including, for academic purposes a VQ strategy in the meta-domain). The algorithms have been rigorously tested for the actual road-travel distances involving cities in T urkiye. The results obtained are not only conclusive, but also the best currently available from any single or hybrid strategy.

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Neural Learning Algorithms and Their Applications in Robotics

Genís¹

1991

Self-Organization, Emerging Properties, and Learning

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Neuroplanners for hand/eye coordination

Cited by 18 publications

References 6 publications

Discrete vector quantization for arbitrary distance function estimation

Discrete vector quantization for arbitrary distance function estimation

Vector Quantization for Arbitrary Distance Function Estimation

Neural Learning Algorithms and Their Applications in Robotics

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