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Artificial neural networks model

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Publisher: BUKOLA

Authors: Bukola Salami

Publish Year: 2016

Pages: 20

ISBN10: 2630-0192

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t This research involves the development of an artificial neural network (ANN) model
that forecasts the weekly production quantities of outputs for a typical cocoa processing company
in order to reduce post-harvest losses. The artificial neural network was initially built with a
single input and a single output with the aid of the Neurosolutions 5.07 software package. It was
then trained, cross- validated and tested by carrying out a successful pilot test using raw
production data obtained from the cocoa processing company. The data set consists of two input
variables and two output variables, and the relationship between any input and output variable is
complex. Input variables are the weekly quantities of cocoa bags tipped and batches of cocoa nibs
roasted, while output variables are weekly quantities of cocoa butter and cocoa cake packaged in
cartons. On training the networks, the parameters of specific networks found to give an
acceptable mean square error (MSE) were recorded. The network was later modified using
different combination types of input(s) and output(s). The model outputs were found to be
satisfactory, lying within the defined error limit when compared to the actual outputs. The result
shows that the network developed was able to predict the output quantities with a high accuracy,
as the training and cross-validation errors at all times both lie within the target error of 0.0001 as
specified by the software developers. The network’s ability in forecasting these outputs with a
high degree of accuracy goes a long way in demonstrating that artificial neural networks are
highly capable of forecasting in situations when there is no closed-formed mathematical
relationship between input and output.

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