Cerebellar Model Associative Computer (cmac) For Gravimetric Geoid Study Based On Egm96 And Eigen-gl04c Geopotential Development

Transcript

In this paper, cerebellar model associative computer (CMAC) algorithm is proposed to be used for geoid study. This algorithm based on the mapping scheme of input space that transforms input similarity in the input space into associated levels in the associated cells space. Similar inputs mapped to similar levels, while dissimilar inputs associated to mutual independent cells. Associated levels given by overlapping base functions selected represent a subset of “feature” to be parameterized using adaptive linear combiner (ALC) structure where a set of “feature” impersonate as input vector in the back side, parameter or weight vector in the middle side, and an output in the front side. ALC structure adjusts weights value using LMS (least mean square) recursion in the training (or analyzing) session, and accumulates weighted input to get an output in the synthesizing session. Desired output using in weights adjustment are any values from the output space that correspond to respective values in the input space. Model assessment test used to validate CMAC model by comparing residual distance of consecutive points from both EGM96 and EIGEN-GL04C geopotential global development is data and respective CMAC model in geocentric coordinates system. By utilization of EGM96 and EIGENGL04C geopotential global development data sets acquired from selected points of geometric geoid in around of Merapi and Merbabu volcanoes, it is simulated the proposed algorithm to study geoid. The results show that difference between data and model based on EGM96 geopotential global development are about 0.00026296 m in total absolute value with standard deviation about 8.3856 x 10(6) m, and based on EIGEN-GL04C geopotential global development is about 0.00027139 m in total absolute value with standard deviation about 8.5441 m. These results use 1x106 epoch in training session with gain factor mu=1.0638 x 10(4) m better result could be achieved by adding more training session with smaller gain factor.