Over the last decades or so, artificial neural networks (ANNs) have become one of the most promising tools for modelling hydrological processes such as rainfall-runoff processes. In most studies, ANNs have been demonstrated to show superior result compared to the traditional modelling approaches. They are able to map underlying relationships between input and output data without detailed knowle...

Biological neurons communicate with a sparing exchange of pulses – spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on recent insights in neuroscience, we present an Adapting Spiking Neural Network (ASNN) based on adaptive spiking n...

This paper presents a new approach to equalization of communication channels using Artificial Neural Networks (ANNs). A novel method of training the ANNs using Tabu based Back Propagation (TBBP) Algorithm is described. The algorithm uses the Tabu Search (TS) to improve the performance of the equalizer as it searches for global minima which is many a time escaped while Back Propagation (BP) algo...

The original project description has been: An existing Java application handling and visualizing Go games between human and computer players (including trained and evolved ANNs) should be improved and extended with Go playing ANNs trained by temporal difference learning. This extension should serve as a basis for comparisons of td learning with conventional ANN training and evolutionary methods...

Artificial neural networks (ANNs) have now been applied to a wide variety of real-world problems in many fields of application. The attractive and flexible characteristics of ANNs, such as their parallel operation, learning by example, associative memory, multifactorial optimization and extensibility, make them well suited to the analysis of biological and medical signals. In this study, we rev...

Artificial neural networks (ANNs) are applied to many real-world problems, ranging from pattern classification to robot control. In order to design a neural network for a particular task, the choice of an architecture (including the choice of a neuron model), and the choice of a learning algorithm have to be addressed. Evolutionary search methods can provide an automatic solution to these probl...

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Artifacts in EEG signals are caused by various factors, like line interference, EOG (electro-oculogram) and ECG (electrocardiogram). The removal of artifact from scalp EEGs is of considerable importance for analysis of underlying brainwave activity. The presence of artifacts such as muscl...

Artificial Neural Networks (ANNs) were used to predict nanoparticle size and micropore surface area of polylactic acid nanoparticles, prepared by a double emulsion method. Different batches were prepared while varying polymer and surfactant concentration, as well as homogenization pressure. Two commercial ANNs programs were evaluated: Neuroshell Predictor, a black-box software adopting both neu...

introduction: with using multiple linear regression (mlr), can simultaneously analyses several different variables, but to get the desirable results from the mlr, the samples must be much and accurate. therefore, this method has high sensitivity and may cause errors in results. in addition, to use this method, the variable must have normal distribution and modification follow from a linear rela...