A ten layer feed-forward network characterized by diverging/converging patterns of projection between successive layers of regular spiking (RS) neurons is activated by an external spatiotemporal input pattern fed to Layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal informati...

The aim of the paper is to develop an edge preserving image compression technique using one hidden layer feed forward neural network of which the neurons are determined adaptively. Edge detection and multi-level thresholding operations are applied to reduce the image size significantly. The processed image block is fed as single input pattern while single output pattern has been constructed fro...

Dealing with uncertainty is one of the most critical problems in complicatedpattern recognition subjects. In this paper, we modify the structure of a useful UnsupervisedFuzzy Neural Network (UFNN) of Kwan and Cai, and compose a new FNN with 6 types offuzzy neurons and its associated self organizing supervised learning algorithm. Thisimproved five-layer feed forward Supervised Fuzzy Neural Netwo...

The `Neural Pipeline' is introduced as an arti cial neural network architecture that controls information ow using its own connection structure. The architecture is multi-layered with `external' connections between the layers to control the data. Excitatory connections transfer data from each layer to the next and inhibitory feedback connections run from each layer to the previous layer. Using ...

  Background :Timely diagnosis of liver cirrhosis is vital for preventing further liver damage and giving the patient the chance of transplantation. Although biopsy of the liver is the gold standard for cirrhosis assessment, it has some risks and limitations and this has led to the development of new noninvasive methods to determine the stage and prognosis of the patients. We aimed to design an...

this paper presents the application of three main artificial neural networks (anns) in damage detection of steel bridges. this method has the ability to indicate damage in structural elements due to a localized change of stiffness called damage zone. the changes in structural response is used to identify the states of structural damage. to circumvent the difficulty arising from the non-linear n...

ground vibrations caused by blasting are undesirable results in the mining industry and can cause serious damage to the nearby buildings and facilities; therefore, controlling such vibrations has an important role in reducing the environmental damaging effects. controlling vibration caused by blasting can be achieved once peak particle velocity (ppv) is predicted. in this paper, the values of p...

a sensitive and selective method using combination of principal component analysis (pca), artificial neural network (ann) and uv-visible spectroscopy has been developed for the simultaneous determination of acetaminophen (amp) and codeine (cod) in plasma samples. the ann trained by the back-propagation learning was employed to model the complex non-linear relationship between the pcs extracted ...