Neural network

SPEAKER IDENTIFICATION AND VERIFICATION OVER SHORT DISTANCE TELEPHONE LINES USING ARTIFICIAL NEURAL NETWORKS Ganesh K Venayagamoorthy, Narend Sunderpersadh, and Theophilus N Andrew gkumar@ieee.org sundern@telkom.co.za theo@wpo.mlsultan.ac.za Electronic Engineering Department, M L Sultan Technikon, P O Box 1334, Durban, South Africa. ABSTRACT Crime and corruption have become rampant today in our society and countless money is lost each year due to white collar crime, fraud, and embezzlement. This

budgeting and controlling if reliable. This paper presents a comparison between two cost estimate predictive modeling, an artificial intelligence approach using neural networks as non-parametric modeling; and multiplely regression modeling that represents advanced engineering statics - a parametric modeling. Results show that neural networks have advantages when dealing with data that for which there is little a priori knowledge of the appropriate cost estimate relationship to select for

training of a neural networks is overfitting towards the input data, which generates low quality predictions [56]. In order to reduce this tendency, the early stopping criteria has been formulated. This involves dividing the input set in three subsets [57]: -training set, used to estimte the weights; -testing set, used to assess the prediction sample and -validation set, which is an accuracy indicator of the prediction sample, although it is not used during the

we have encountered one major problem that is how to interpret a neural network given its black box characteristics. We really wanted to try ourselves giving interpretation to our results so that we dug into the existing literature and found out a very interesting research paper written by Garson in 1991. In « Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks », Olden and al. describes Garson’s algorithm very concisely so that

chapter the radius for a given resonant frequency of a centre feed circular microstrip patch antenna has been analysed using two layer neural network structures FFBP ANN model. The Levenberg – Marquardt training algorithm and the transfer function tansig have been used to implement the neural network model. The simulated values for training and testing the neural network model are obtained by analysing the circular microstrip patch antenna using CST Microwave Studio Software (CST-MWS). The results obtained

Chapter 1 Quantum Neural Network 1.1 Introduction and Background The eld of articial neural networks (ANNs) draws its inspiration from the working of human brain and the way brain processes information. An ANN is a directed graph with highly interconnected nodes called neurons.Each edge of the graph has a weight associated with it to model the synaptic eciency. The training process involves updating the weights of the network in such a way that the network learns to solve the problem

Introduction Artificial neural networks are a class of computational structures (Lesk, 2013) made up of several processing elements, called artificial neurons that are connected and organized in layers (Larder et al., 2007). They are capable of generating models for the detection of non linear functions(..). Their algorithms are extensively applied in biology and medicine to solve complex problems, more specifically for prediction or classification of solutions or to refine methodological aspects

we have encountered one major problem that is how to interpret a neural network given its black box characteristics. We really wanted to try ourselves, giving interpretation of our results so that we dug into the existing literature and found out a very interesting research paper written by Garson in 1991. In « Illuminating the black box: a randomization approach for understanding variable contributions in artificial neural networks », Olden and al. describes Garson’s algorithm very concisely so that

This research Paper has problems with formatting ABSTRACT Current neural network technology is the most progressive of the artificial intelligence systems today. Applications of neural networks have made the transition from laboratory curiosities to large, successful commercial applications. To enhance the security of automated financial transactions, current technologies in both speech recognition and handwriting recognition are likely ready for mass integration into financial institutions

Artificial Intelligence Computers are everywhere today. It would be impossible to go your entire life without using a computer. Cars, ATMs, and TVs we use everyday, and all contain computers. It is for this reason that computers and their software have to become more intelligent to make our lives easier and computers more accessible. Intelligent computer systems can and do benefit us all; however people have constantly warned that making computers too intelligent can be to our disadvantage