A tool for process identification and neural network simulation by means of a personal digital assistant

Abstract

This paper describes the design, implementation and testing of the software tool designated as UV-SRNA-PDA (Universidad del Valle’s artificial neural network simulator for a personal digital assistant), which is oriented to artificial neural networks simulation and complex industrial processes identification. This application works on a PDA (personal digital assistant) Palm T5 using a customized data acquisition system. Two kinds of artificial neural networks were implemented: perceptron and multi-layer perceptron (MLP) using as learning algorithms the following: backpropagation, descendent gradient, variable learning rate and momentum. To test the proposed system, first and second order systems were selected (only the latter is reported here), finding their neural models and validating their results with MATLAB and UV-SRNA 2.0 (PC version of UV-SRNA-PDA). These tests achieved an average training error of 5.62 × 10-3 ± 3.55 × 10-4 and an average validating error of 4.56 × 10-3 ± 5.95 × 10-4. In both cases, the results were better or comparable with those from other software simulation tools. However, the typical training time on the UV-SRNA-PDA was 900 s compared to 3 s in MATLAB and 8 s in UV-SRNA 2.0.