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Back-Propagation MLP Neural Network Optimizer

Back-Propagation MLP Neural Network Optimizer. ECE 539 Andrew Beckwith. Back-Propagation MLP Network Optimizer. Purpose Methods Features. Purpose. Configuring a Neural Network and its parameters is often a long and experimental process with much guess work. Let the computer do it for you.

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Back-Propagation MLP Neural Network Optimizer

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  1. Back-Propagation MLP Neural Network Optimizer ECE 539 Andrew Beckwith

  2. Back-Propagation MLP Network Optimizer • Purpose • Methods • Features

  3. Purpose • Configuring a Neural Network and its parameters is often a long and experimental process with much guess work. • Let the computer do it for you. • Design and implement a program that can test multiple network configurations with easy setup. • Allow user to modify data properly by enhancing important features and minimizing features with little importance or detrimental qualities.

  4. Methods • Use back-propagation algorithm with momentum • To test multiple configurations, use brute force method and keep track of most successful configuration. • Only parameter user cannot control is the number of neurons per hidden layer. • Each configuration is tested with 2, 3, 5, and 10 neurons per hidden layer. The last test is a random initialization between 1 and 10 for each layer. • Use hyperbolic tangent activation function for hidden neurons and sigmoidal activation function for output neurons. One could change this in the source code if desired.

  5. Features • Allow user to open data file, view mean and standard deviation for each feature of each class for modification purposes. • Allow user to enter ranges and number of trials for parameters such as: max epoch, epoch size, learning rate, momentum constant, and the number of hidden layers. • Allow user to set a tolerance to achieve maximum classification rate. • Allow user to view entire network – network configuration, weight values, etc.

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