Introducing Artificial Neural Networks through a Spreadsheet Model

Rienzo, Thomas; Athappilly, Kuriakose

doi:10.1111/j.1540-4609.2012.00363.x

Cited by 4 publications

(8 citation statements)

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“…A network has an input layer, an output layer, and one or more "hidden" layers in between, necessary to allow solutions of non-linear problems. Each unit (in some ways analogous to a biological neuron: dendrites -input layer, axon -output layer, synapses -weights [43], soma -summation function) is capable of generating an output signal which is determined by the weighted sum of input signals it receives and an activation function specific to that unit. A unit is provided with inputs, either from outside the network or from other units, and uses these to compute a linear or non-linear output.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In their article of 2012 [43], Thomas F. Rienzo and Kuriakose K. Athappilly from Haworth College of Business at Western Michigan University consider model illustrating the process of machine learning as networks examine training data would provide another. Authors incorporate the stepwise learning processes of artificial neural network in a spreadsheet containing (1) a list or table of training data for binary input combinations, (2) rules for target outputs, (3) initial weight factors, (4) threshold values, (5) differences between target outputs and neural network transformation values, (6) learning rate factors, and (7) weight adjustment calculations.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The conducted review makes it possible to find the following solutions of the problem of computer simulation teaching to neural networks in the spreadsheet environment: -joint application of spreadsheets and neural network tools [32], in which data is exported to the unit calculating weighting factors imported to spreadsheets and used in calculations; -application of third-party add-ins for spreadsheets ([15; 23; 45]), according to which structured spreadsheet data is processed in the add-in, calculation results are arranged in spreadsheet cells; -macros development ([7; 14; 55; 57]) enables direct software control over neural network training and creation of a user's specialized interface; -application of standard add-ins for optimization ( [15; 24]) calls for transparent network realization and determination of an optimization criterion (minimization of a squared deviation total of the calculated and etalon outputs of the network); -creation of neural networks in the spreadsheet environment without add-ins and macros [43] requires transparent realization of a neural network with evident de-termination of each step of adjustment of its weighting factors.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Using spreadsheets as learning tools for computer simulation of neural networks

et al. 2020

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The article substantiates the necessity to develop training 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 with-out add-ins and macros. The article considers ways of building neural network models in cloud-based spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional 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. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Section: Literature Review and Problem Statementmentioning

confidence: 99%

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Using spreadsheets as learning tools for computer simulation of neural networks

et al. 2020

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“…A network has an input layer, an output layer, and one or more "hidden" layers in between, necessary to allow solutions of non-linear problems. Each unit (in some ways analogous to a biological neuron: dendrites -input layer, axon -output layer, synapses -weights [47], soma -summation function) is capable of generating an output signal which is determined by the weighted sum of input signals it receives and an activation function specific to that unit. A unit is provided with inputs, either from outside the network or from other units, and uses these to compute a linear or non-linear output.…”

Section: Introductionmentioning

confidence: 99%

“…In their article of 2012 [47], Thomas F. The conducted review makes it possible to find the following solutions of the problem of computer simulation teaching to neural networks in the spreadsheet environment: ─ joint application of spreadsheets and neural network tools [29], in which data is exported to the unit calculating weighting factors imported to spreadsheets and used in calculations;…”

Section: Introductionmentioning

confidence: 99%

Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot

Семеріков

Теплицький

Єчкало

et al. 2018

EducDim

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Semerikov S.O., Teplytsʹkyy I.O., Yechkalo YU.V. and Kiv A.E. Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot. The article substantiates the necessity to develop training 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. After analyzing a collection of writings of 1890–1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. 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. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods — the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

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Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot

Semerikov¹,

Teplytskyi²,

Yechkalo³

et al. 2018

View full text Add to dashboard Cite

The article substantiates the necessity to develop training 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. After analyzing a collection of writings of 1890-1950, the research determines the role of the scientific journal “Bulletin of Mathematical Biophysics”, its founder Nicolas Rashevsky and the scientific community around the journal in creating and developing models and methods of computational neuroscience. There are identified psychophysical basics of creating neural networks, mathematical foundations of neural computing and methods of neuroengineering (image recognition, in particular). The role of Walter Pitts in combining the descriptive and quantitative theories of training is discussed. 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. It is indicated that there are three groups of models, which are promising in terms of developing corresponding methods – the continuous two-factor model of Rashevsky, the discrete model of McCulloch and Pitts, and the discrete-continuous models of Householder and Landahl.

show abstract

Introducing Artificial Neural Networks through a Spreadsheet Model

Cited by 4 publications

References 4 publications

Using spreadsheets as learning tools for computer simulation of neural networks

Using spreadsheets as learning tools for computer simulation of neural networks

Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot

Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot

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