A Comparison of Classification Accuracy for Gender Using Neural Networks Multilayer Perceptron (MLP), Radial Basis Function (RBF) Procedures Compared to Discriminant Function Analysis and Logistic Regression Based on Nine Sports Psychological Constructs to Measure Motivations to Participate in Masters Sports Competing at the 2009 World Masters Games

ABSTRACT Using the data describing the characteristics of contractors provided by the Comptroller General of the Union, Brazil (CGU), this paper mainly implements two artificial neural networks, traditional neural network (TNN) and deep neural network (DNN), to develop prediction models of public procurement irregularities designed for the initial screening of contractors. This is the first application of DNN in the context of government auditing. To examine the effectiveness of DNN, the authors compare its predictive performance to TNN and two other algorithms (logistic regression and discriminant function analysis) and find that DNN significantly outperforms TNN and other algorithms in terms of accuracy, precision, F-scores, AUC, and other metrics, as suggested by the high Z-scores of the Z-tests. Although TNN has a higher recall than DNN, the difference of recall between TNN and DNN is insignificant. Logistic regression and discriminant function analysis achieve the highest recall scores, but their Z-scores are much lower than those of other metrics. Therefore, DNN generally performs more accurately than other approaches and meets the requirement of the CGU for an early alarm system.

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Implementation of multilayer perceptron (MLP) and radial basis function (RBF) neural networks to predict solution gas-oil ratio of crude oil systems

Petroleum ◽

10.1016/j.petlm.2018.12.002 ◽

2020 ◽

Vol 6 (1) ◽

pp. 80-91 ◽

Cited By ~ 7

Author(s):

Aref Hashemi Fath ◽

Farshid Madanifar ◽

Masood Abbasi

Keyword(s):

Neural Networks ◽

Crude Oil ◽

Radial Basis Function ◽

Basis Function ◽

Multilayer Perceptron ◽

Rbf Neural Networks ◽

Gas Oil ◽

Radial Basis ◽

Solution Gas

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Fast Learning in Neural Networks

Biologically Inspired Artificial Intelligence for Computer Games ◽

10.4018/978-1-59140-646-4.ch006 ◽

2008 ◽

pp. 91-104

Author(s):

Darryl Charles ◽

Colin Fyfe ◽

Daniel Livingstone ◽

Stephen McGlinchey

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Continuous Function ◽

Radial Basis Function ◽

Supervised Learning ◽

Basis Function ◽

Multilayer Perceptron ◽

Rbf Networks ◽

Rbf Network ◽

Fast Learning

We noted in the previous chapters that, while the multilayer perceptron is capable of approximating any continuous function, it can suffer from excessively long training times. In this chapter we will investigate methods of shortening training times for artificial neural networks using supervised learning. (Haykin, 1999) is a particularly good reference for radial basis function, RBF, networks. In this chapter we outline the theory and implementation of a RBF network before demonstrating how such a network may be used to solve one of the previously visited problems, and compare our solutions.

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