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Sensor for Classification of Material Type and Its Surface Properties Using Radial Basis Networks

机译：径向基网络的材料类型及其表面性质分类传感器

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摘要

An application of the power of neural networks in the implementation of a novel sensor for classification of material type and its surface properties by means of a lightweight plunger probe and optical mouse sensor is presented in this paper. An experimental prototype was developed which involves bouncing or hopping of the plunger-based impact probe freely on the plain surface of an object under test. During the bouncing of the probe, a time-varying signal is generated from optical mouse that is recorded in a data file on PC. Features of the signals are then extracted using signal processing tools to optimize neural network-based classifier used in the existing system. The classifier is developed using radial basis function neural networks (RBF NNs). For this, an optimum RBF NN model is designed to maximize accuracy under the constraints of minimum network dimension. Levenberg-Marquardt learning algorithm, which provides faster rate of convergence, has been found suitable for the training of RBF NN. The optimal parameters of RBF NN model based on classification accuracy on the testing data sets even after attempting different data partitions are determined. The classification accuracy of RBF NN is found consistently reasonable in respect of rigorous testing using different data partitions, and multifold cross-validation.
机译：本文介绍了神经网络的功能在实现新型传感器中的应用，该传感器通过轻质柱塞探针和光学鼠标传感器对材料类型及其表面特性进行分类。开发了一个实验原型，其中涉及将基于柱塞的冲击探针自由地弹跳或跳到被测物体的平面上。在探头弹起期间，光学鼠标会生成随时间变化的信号，该信号会记录在PC上的数据文件中。然后使用信号处理工具提取信号特征，以优化现有系统中使用的基于神经网络的分类器。使用径向基函数神经网络（RBF NN）开发分类器。为此，设计了最佳的RBF NN模型，以在最小网络尺寸的约束下最大程度地提高准确性。提供更快收敛速度​​的Levenberg-Marquardt学习算法已被发现适合于RBF NN的训练。即使在尝试了不同的数据分区之后，仍会基于测试数据集的分类精度确定RBF NN模型的最佳参数。对于使用不同数据分区的严格测试以及多重交叉验证，发现RBF NN的分类准确性始终是合理的。

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