The developed Neural Network Simulator using backpropagation achieved 99% accuracy on basic logic gate data.
Neural Networks (NN) are computational models with the capacity to learn, to generalize, or to organize data based on parallel processing. Among all kinds of networks, the most widely used are multi-layer feed-forward Neural Networks that are capable of representing non-linear functional mappings between inputs and outputs and are hailed as “Universal Approximators”. These networks can be trained with a powerful and computationally efficient method called error backpropagation. This paper presents the development of Neural NetworkSimulator using Backpropagation algorithm using Visual Basic 6.0. The methodology used in this development is System Development that consists of five phases. The phases include Preliminary Study, Analysis, Design, Implementation and Maintenance. Testing has been made on logic gate data AND, OR and XOR. Results show that the Neural Networks is 99% accurate.
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Wahab et al. (2016) studied this question.
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