The Improved Radial Basis To The Research And Application Of Three Dimensional Interpolation Algorithm

Due to mineral resources development projects, the grade of the ore bodydemand prediction and reserves estimation.At the same time, subject to projectlimited budget and other objective factors, the general sample point data areinsufficient to fully reflect the distribution of ore bodies.Under the objectiveconditions permit and as far as possible to meet project requirements, so you need touse spatial interpolation techniques to make up for the sampling point datainformation is not comprehensive.However, such as inverse distance weightedkriging interpolation method in essence, belong to according to the theory of linearsliding weighted average method, so the local easy result caused by the data forsmooth operation of the loss of information, in addition, for the nonlinearcharacteristics of the description of the spatial distribution of ore grade also hascertain restrictions on accuracy, and use of kriging method is satisfy the intrinsichypothesis or stationary hypothesis condition, the sampling data obey the lognormaldistribution or normal distribution.Because the reality is difficult to meet the aboverequirements.So urgent need for a system of nonlinear processing ability strong andthe sampled data distribution and the method of assuming that there is norequirement.RBF network is a kind of block interpolation method.First of all, itsradial base on the sample data classification, and based on points good categoryrespectively for each type of data for training.This is equivalent to actual originallyvery complex problem can be divided into several relatively complex problemsolving respectively, the feasibility is obviously higher.However, RBF network issubject to the initial center is difficult to identify and solve complex problems hiddenlayer nodes of too much trouble.Aiming at these problems for RBF neural networkto determine a more optimized initial center, enhance the performance of the RBFnetwork, by means of improved simulated annealing ant colony algorithm for radialbasic training method of RBF neural network, so that more optimization for RBFmodel in theory the initial center, the problem will originally very complicated tosimplify, this also fit the space characteristic of the geological ore body, for thetraditional ore grade prediction using more advanced machine learning algorithms provided possibility, probably this new method can bring more energy thantraditional method.Then improved RBF neural network model was applied to theface of the ground elevation interpolation and orebody savoured space interpolation,and cross check with inverse distance weighting algorithm, optimization effect isobvious, at the same time, using vc++and OpenGL development environment todevelop orebody visualization system, in combination with ore bodies of theexample of actual data, the application process, actual effect is obvious.At the sametime also for related researchers for its space distribution rule and the deeper layer ofresearch provide a more convenient tool.So the application prospect of this methodis very valuable.