Log-sigmoid Activation Function based MLP Network for Aggregate Classification

Mechanical sifting and manual grading have conventionally been utilised to assess the grade of aggregates. Nonetheless, such evaluations require a range of mechanical, chemical, and physical examinations, typically conducted manually, resulting in a process that is tedious, subjective, and labour-intensive. This research aims to provide an image-based classification system for the categorisation of aggregates. An artifi cial neural network (ANN) has been used to analyse the acquired images and categorise their shapes. The composite images are obtained and utilised as the input parameter for prediction prior to the thresholding step. The Log-sigmoid (Logsig) activation function, utilised in a Multilayer Perceptron (MLP) network, exhibits a lower mean square error (MSE) and superior regression performance relative to the Pureline activation functions. The Logsig-based network has a MSE of 1.7473 and a regression capability of 0.9521.

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