Over the last few years, artificial neural networks (ANNs) have been used successfully for modeling almost all aspects of geotechnical engineering problems. Whilst ANNs provide a great deal of promise, they suffer from a number of shortcomings such as knowledge extraction, extrapolation and uncertainty. This paper presents a state-of-the-art examination of ANNs in geotechnical engineering and p...

For continuous monitoring of river water quality , this study assesses the potential of using artificial neural networks (ANNs) for modeling the event-based suspended sediments concentration (SSC) in Jiasian diversion weir in southern Taiwan. The hourly data collected include the water discharge, turbidity and SSC during the storm events. The feed forward backpropagation network (BP), generaliz...

Artificial neural networks have featured in a wide range of medical journals, often with promising results. This paper reports on a systematic review that was conducted to assess the benefit of artificial neural networks (ANNs) as decision making tools in the field of cancer. The number of clinical trials (CTs) and randomised controlled trials (RCTs) involving the use of ANNs in diagnosis and p...

DOWNLOAD http://bit.ly/1OslRBc Artificial neural networks: theory and applications This comprehensive tutorial on artifical neural networks covers all the important neural network architectures as well as the most recent theory-e.g., pattern recognition, statistical theory, and other mathematical prerequisites. A broad range of applications is provided for each of the architectures. Artificial ...

This paper focuses on the Genetic Algorithm learning paradigm applied to train the ANNs for balancing the cart-pole balancing system. The studied system is a control problem namely “cart-pole” problem. We will apply the unconventional techniques Artificial Neural Network, Genetic Algorithm and Fuzzy Logic to a classic control problem “cart-pole”. In this paper we have tried to train the Artific...

in recent years, various time series models have been proposed for financial markets forecasting. in each case, the accuracy of time series forecasting models are fundamental to make decision and hence the research for improving the effectiveness of forecasting models have been curried on. many researchers have compared different time series models together in order to determine more efficient ...

1.1 Artificial neural networks in clinical medicine In Medicine, several tools have been developed for the prediction of clinical outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR), however, this method requires formal training and a profound knowledge of statistics (Royston, 2000; Harrel et al., 1996)...

In this paper, we investigate the relation between Artificial Neural Networks (ANNs) and networks of populations of spiking neurons. The activity of an artificial neuron is usually interpreted as the firing rate of a neuron or neuron population. Using a model of the visual cortex, we will show that this interpretation runs into serious difficulties. We propose to interpret the activity of an ar...

The implementation of artificial neural networks (ANNs) to the analysis of multivariate data is reviewed, with particular reference to the analysis of pyrolysis mass spectra. The need for and benefits of multivariate data analysis are explained followed by a discussion of ANNs and their optimisation. Finally, an example of the use of ANNs for the quantitative deconvolution of the pyrolysis mass...

The text area is 29 × 45 picas (~12.3 cm × 19 cm) to fit in the printed proceedings book without reduction. When printed on letter paper there will be wide margins. Abstract. Previous use of Artificial Intelligence (AI) in agriculture for forecasting productivity indicators, especially Artificial Neural Networks (ANN), has shown that it is possible to approximate sugarcane maturation curves. Ho...