Resilient Backpropagation Neural Network on Prediction of Poverty Levels in South Sulawesi

Poverty is a topic that continues and is always discussed up to this time, as a benchmark indicator of how the level of welfare and prosperity in the lives of people in a country. Several attempts have been made by the central and regional governments to reduce poverty levels, including “Bantuan Langsung Tunai” (BLT) and the “Program Keluarga Harapan” (PKH). However, poverty reduction in Indonesia is still slowing down, including in South Sulawesi. Based on this, this study aims to predict poverty levels in South Sulawesi. Factors thought to influence poverty levels are the Human Development Index (HDI), the Open Unemployment Rate (TPT), and the Gross Regional Domestic Product (GRDP). The data used are data from 2010 to 2014. The method used is a backpropagation neural network with a resilient algorithm or better known as a resilient backpropagation neural network (RBNN). The results of the prediction of poverty levels using predictors of HDI, TPT, and GRDP showed that the analysis of the RBNN reached its optimum using architecture [3- 9 - 1] and reached convergence at the 81th iteration with an accuracy rate of 95.34%

ARTIFICIAL NEURAL NETWORK APPROACH FOR THE IDENTIFICATION OF CLOVE BUDS ORIGIN BASED ON METABOLITES COMPOSITION

This paper examines the use of an artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for an accurate identification. Machine learning with large data sets lead to a precise identification based on origins. However, clove buds uses small data sets due to the lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies the clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively

Artificial Neural Network for Location Estimation in Wireless Communication Systems

In a wireless communication system, wireless location is the technique used to estimate the location of a mobile station (MS). To enhance the accuracy of MS location prediction, we propose a novel algorithm that utilizes time of arrival (TOA) measurements and the angle of arrival (AOA) information to locate MS when three base stations (BSs) are available. Artificial neural networks (ANN) are widely used techniques in various areas to overcome the problem of exclusive and nonlinear relationships. When the MS is heard by only three BSs, the proposed algorithm utilizes the intersections of three TOA circles (and the AOA line), based on various neural networks, to estimate the MS location in non-line-of-sight (NLOS) environments. Simulations were conducted to evaluate the performance of the algorithm for different NLOS error distributions. The numerical analysis and simulation results show that the proposed algorithms can obtain more precise location estimation under different NLOS environments

Applying Rprop Neural Network for the Prediction of the Mobile Station Location

Wireless location is the function used to determine the mobile station (MS) location in a wireless cellular communications system. When it is very hard for the surrounding base stations (BSs) to detect a MS or the measurements contain large errors in non-line-of-sight (NLOS) environments, then one need to integrate all available heterogeneous measurements to increase the location accuracy. In this paper we propose a novel algorithm that combines both time of arrival (TOA) and angle of arrival (AOA) measurements to estimate the MS in NLOS environments. The proposed algorithm utilizes the intersections of two circles and two lines, based on the most resilient back-propagation (Rprop) neural network learning technique, to give location estimation of the MS. The traditional Taylor series algorithm (TSA) and the hybrid lines of position algorithm (HLOP) have convergence problems, and even if the measurements are fairly accurate, the performance of these algorithms depends highly on the relative position of the MS and BSs. Different NLOS models were used to evaluate the proposed methods. Numerical results demonstrate that the proposed algorithms can not only preserve the convergence solution, but obtain precise location estimations, even in severe NLOS conditions, particularly when the geometric relationship of the BSs relative to the MS is poor

Empirical analysis of neural networks training optimisation

A Dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Mathematical Statistics,School of Statistics and Actuarial Science. October 2016.Neural networks (NNs) may be characterised by complex error functions with attributes such as saddle-points, local minima, even-spots and plateaus. This complicates the associated training process in terms of efficiency, convergence and accuracy given that it is done by minimising such complex error functions. This study empirically investigates the performance of two NNs training algorithms which are based on unconstrained and global optimisation theories, i.e. the Resilient propagation (Rprop) and the Conjugate Gradient with Polak-Ribière updates (CGP). It also shows how the network structure plays a role in the training optimisation of NNs. In this regard, various training scenarios are used to classify two protein data, i.e. the Escherichia coli and Yeast data. These training scenarios use varying numbers of hidden nodes and training iterations. The results show that Rprop outperforms CGP. Moreover, it appears that the performance of classifiers varies under various training scenarios.LG201

Target detection through image processing and resilient propagation algorithms

This paper deals with target detection studies using the image processing method as well as resilient propagation-based neural network paradigm. In the resilient propagation-based algorithms, the pre-processing operation to extract features of relevance is done using the moment invariance method. These features are then fed as input to the resilient propagation neural network. RPROP (resilient propagation) is an adaptive technique based on the standard backpropagation algorithm. This RPROP algorithm is also implemented in ADSP-21062 assembly language, since a digital signal processor (DSP) execution is much faster than the normal PC execution, as speed is desirable in real time. It is observed that the resilient propagation-based target detection is better compared to the image processing method of target detection. The main objectives of the paper are the demonstration of the applicability of moment invariant features to neural network-based target detection method and implementation of the technique using a DSP chip,ADSP-21062