マイクロ多機能接触型センサによる測定データを学習した自己組織化マップに基づいた材質識別

概要

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In general, a material discrimination task is achieved by following two steps: One is an analytical step for estimating constructive elements of the object, and the other is a computational step for identifying its material. The first step is simple because only measuring plural parameters is required to reach the correct answer. But the second step is complex and various kinds of techniques have been proposed. Then, one of the neural network models called a self-organizing map (SOM), a good tool for topology-preserving projection, is adopted in this paper. Firstly, each material is examined with a micro multi-functional tactile sensor. Secondly, in the training stage, the average data of several measurement trials are applied to the SOM. Finally, in the test stage, the actual measurement raw data, which are completely new to the trained SOM, are applied and the measurement object is estimated from the location of the emerging “winner” neuron. As a result of computer simulations, it is found xperimentally that measured information space is divided into several regions representing each material successfully. Furthermore, an acquired feature map for material discrimination is useful against several untraining data.
2003-01-01