This Labview Project is an implementation of an Artificial Neural Network (ANN) that I made for the "Soft Computing" course of the Master in Intelligent Systems degree.
The goal of this ANN implemented in LABVIEW is to predict the power output of a Hydroelectric Turbine, given a set of known turbine power outputs (Megawatts) patterns corresponding to a set of 4 input variables: Height Above Sea Level, Fall, Net Fall, and Flux.
I coded a customizable ANN architecture, where the user is free to define the number of layers, and neurons per layer, learning rate, momentum, number of epochs and a target MSE (mean squared error) for the training algorithm. The implementation uses extensive vector algebra, gradient operations and mathematical formulas and without using any neural network related toolkits.
A ANN is a mathematical/computational model that is inspired by the structure and/or functional aspects of biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. They are usually used to model complex relationships between inputs and outputs or to find patterns in data. Backpropagation is a common method of training artificial neural networks. Backpropagation calculates the gradient of the error of the network regarding the network's modifiable weights. This gradient is used in a simple stochastic gradient descent algorithm to find weights that minimize the error.
I am a Mechatronics Engineer from UNITEC, Honduras and MSc of Intelligent Systems from Universitat Rovira i Virgili, Spain
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