The Artificial Neural Networks (ANNs) are based on the behaviour of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved i...

Artificial Neural Networks (ANNs) have been successfully applied to Optical Character Recognition (OCR) yielding excellent results. This paper presents a method for segmentation of difficult handwriting with the use of conventional algorithms in conjunction with ANNs. The segmentation algorithm is heuristic in nature detecting important features which may represent a prospective segmentation po...

A very timely issue for economic agent-based models (ABMs) is their empirical estimation. This paper describes a line of research that could resolve the issue by using machine learning techniques, using multi-layer artificial neural networks (ANNs), or so called Deep Nets. The seminal contribution by Hinton et al. (2006) introduced a fast and efficient training algorithm called Deep Learning, a...

Mathematical modelling is used routinely to understand the coding properties and dynamics of responses of neurons and neural networks. Here we analyse the effectiveness of Artificial Neural Networks (ANNs) as a modelling tool for motor neuron responses. We used ANNs to model the synaptic responses of an identified motor neuron, the fast extensor motor neuron, of the desert locust in response to...

Models and algorithms have been designed to mimic information processing and knowledge acquisition of the human brain generically called artificial or formal neural networks (ANNs), parallel distributed processing (PDP), neuromorphic or connectionist models. The term network is common today: computer networks exist, communications are referred to as networking, corporations and markets are stru...

over the last decade or so, artificial neural networks (anns) have become one of the most promising tools formodelling hydrological processes such as rainfall runoff processes. however, the employment of a single model doesnot seem to be an appropriate approach for modelling such a complex, nonlinear, and discontinuous process thatvaries in space and time. for this reason, this study aims at de...

Artificial neural networks, ANNs, have been widely used especially in the last two decades. ANNs have a wide range of scientific applications, they are used for pattern recognition and forecasting of natural mechanisms. In water treatment, ANNs have enormous potential, especially to support workers in plant operation. In clear water plants are taking large volumes of data, especially informatio...

Feed-forward Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modelling complex real–world problems. One of the latest research areas in this field is evolving ANNs. In this paper, we investigate the simultaneous evolution of architectures and connection weights in ANNs. In simultaneous evolution, we use the concept of multiobjective optimization and...

This paper describes the time variant changes in satellite images using Self Organizing Feature Map (SOFM) technique associated with Artificial Neural Network. In this paper, we take a satellite image and find the time variant changes using above technique with the help of MATLAB. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main co...

the emphasis of this paper is the role of volatility indices on improvement artificial neural networks (anns) forecasting models for the daily usd/eur and usd/gbp exchange rates two volatility indices are used. first; the realized volatility, which is based on intra-daily data, and second the garch volatility. they are applied into the model in two ways. firstly, the lagged volatility index is ...