Abstract
The article substantiates the necessity to develop methods of computer simulation of neural networks in the spreadsheet environment. The systematic review of their application to simulating artificial neural networks is performed. The authors distinguish basic approaches to solving the problem of network computer simulation training in the spreadsheet environment, joint application of spreadsheets and tools of neural network simulation, application of third-party add-ins to spreadsheets, development of macros using the embedded languages of spreadsheets; use of standard spreadsheet add-ins for non-linear optimization, creation of neural networks in the spreadsheet environment without add-ins and macros. It is shown that to acquire neural simulation competences in the spreadsheet environment, one should master the models based on the historical and genetic approach. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multidimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s–1930s is discussed as well as some peculiarities of data selection.
Highlights
APPLICATION OF CLOUD-BASED suggests storing input data inFor the past years, the authors have been developing the concept of systematic computerSPREADSHEETS TO ARTIFICIAL NEURAL NETWORK MODELLING columns, maximum and minimum values for each column of input data, the number of learnsimulation training at univer-Oksana Markova ing patterns
It conditions the need for sheet environment, joint application of spreadsheets and than 2,000 individuals belonging developing training methods of tools of neural network simulation, application of third-par- to 100 populations, data far more computer simulation of neural ty add-ins to spreadsheets, development of macros using the extensive than those that any botnetworks in the general-purpose embedded languages of spreadsheets; use of standard spread- anist had yet obtained on a sinsimulation environment, i.e. sheet add-ins for non-linear optimization, creation of neural gle species
Neural network training is performed by varying weight coefficients so that with each training step the difference between the calculated values of the output layer and the desired reduces
Summary
APPLICATION OF CLOUD-BASED suggests storing input data inFor the past years, the authors have been developing the concept of systematic computerSPREADSHEETS TO ARTIFICIAL NEURAL NETWORK MODELLING columns, maximum and minimum values for each column of input data, the number of learnsimulation training at univer-Oksana Markova ing patterns. 3. Results Let’s first introduce Anderson’s Irises into spreadsheets with the following values of cells: A1 is Iris Data, A2 is SL, B2 is SW, C2 is PL, D2 is PW, E2 is Species.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
