Learning to Approach a Moving Ball with a Simulated Two-Wheeled Robot

Flentge, Felix

doi:10.1007/11780519_10

Cited by 1 publication

(2 citation statements)

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“…Producer node computes kernel matrix according to formula (13) and (14) Equation (13) and (14) allow us to parallelize the kernel computation with minimal communication cost.…”

Section: Distributed Kernel Algorithmmentioning

confidence: 99%

“…The local weighted learning method seems a promising solution to the aforementioned engineering project, where an accurate nonlinear global model is difficult to establish. Although this method has proved to be effective and is widely used in robotic control [11,12,13], only limited work was done to apply the method to the intelligent decision system modelling in metallurgical processes.…”

Section: Introductionmentioning

confidence: 99%

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An intelligent model of LWA using distributed kernel

Wang

Liao

Cao

2008

2008 7th World Congress on Intelligent Control and Automation

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This paper researches the possibility of using locally weighted algorithm for intelligent modeling of a nonlinear system for vanadium extraction in metallurgical process and proposes some optimized methods by finding the optimized regression coefficients by gradient descent and kernel function bandwidth by weighted distance. But kernel matrix computation for high dimensional data source demands heavy computing power. To overcome the computational difficulties of kernel functions and shorten the computing time, the paper designs a distributed algorithm to compute the kernel function matrix of LWA. The paper then implements the algorithm on a cluster of computing workstations using MPI. This paper studies the possibility of LWA using distributed kernel computing for predictive modeling for vanadium extraction in metallurgical process. Finally, the practical data are used to study the speedups and accuracy of the algorithm. The experimental results show that optimized locally weighted algorithm using distributed kernel outperforms the traditional RBF, RFWR and LWPR methods when significant amounts of noise are added, and the computing time has been shortened. Index Terms -locally weighted algorithm; distributed kernel computing; gradient descent; weighted distance; intelligent model II. LOCALLY WEIGHTED ALGORITHM AND OPTIMIZED REGRESSION COEFFICIENTSLocally weighted algorithm is derived from standard linear regression. This algorithm fits a surface to "local" points using distance-weighted regression. The locally weighted algorithm 978-1-4244-2114-5/08/$25.00

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“…Producer node computes kernel matrix according to formula (13) and (14) Equation (13) and (14) allow us to parallelize the kernel computation with minimal communication cost.…”

Section: Distributed Kernel Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%