This paper presents a feed forward back-propagation neural network model to predict the retained tensile strength and design chart in order to estimation of the strength reduction factors of nonwoven geotextiles due to installation process. A database of 34 full-scale field tests were utilized to train, validate and test the developed neural network and regression model. The results show that t...

We develop a systems theoretical treatment of a behavioural system that interacts with its environment in a closed loop situation such that its motor actions influence its sensor inputs. The simplest form of a feedback is a reflex. Reflexes occur always “too late”; i.e., only after a (unpleasant, painful, dangerous) reflex-eliciting sensor event has occurred. This defines an objective problem w...

In this paper, a learning control system is considered for motion systems that are subject to two types of disturbances; reproducible disturbances, that re-occur each run in the same way, and random disturbances. In motion systems, a large part of the disturbances appear to be reproducible. In the control system considered, the reproducible disturbances are compensated by a learning component c...

In a 4T pixel, the transfer gate (TG) “OFF” surface potential is one of the important parameters, which determines the pinned photodiode (PPD) full well capacity. The feed-forward effect measurement is a powerful tool to characterize the relationship of the PPD injection potential and the feed-forward electrons. In this paper, a parameter Vb is introduced to characterize the TG “OFF” surface po...

a neural network with feed forward topology and back propagation algorithm was used to investigate the effect of composition on mechanical properties in api x65 microalloyed steel (used in manufacturing of large diameter pipes). experimental data was obtained by cutting 100 specimens from pipes manufactured in industrial scale (with similar heats and manufacturing processes). the chemical analy...

The main objective of this project is cell nuclei segmentation and classification. In pre-processing convert the gray scale and apply the segmentation. In segmentation this project proposes the graph cut algorithm. After segmentation the features are extracts such as texture and etc. In classification this project used the feed forward neural network. The proposed method is very efficient. The ...

An artificial neural network can be used as an intelligent controller to control non-linear, dynamic system through learning. It can easily accommodate non-linearities and time dependencies. Most common multi-layer feed-forward neural networks have the drawbacks of large number of neurons and hidden layers required to deal with complex problems and require large training time. To overcome these...

In this study, the use of the three-layer feed forward neural network has been investigated for estimating of infinite dilute diffusion coefficient ( D12 ) of supercritical fluid (SCF), liquid and gas binary systems. Infinite dilute diffusion coefficient was spotted as a function of critical temperature, critical pressure, critical volume, normal boiling point, molecular volume in normal boilin...

In this work, the artificial neural networks (ANN) technology was applied to the simulation of oleuropein extraction process. For this technology, a 3-layer network structure is applied, and the operation factors such as  amount  of  flow  intensity  ratio,  temperature,  residence  time,  and  pH  are  used  as  input  variables  of  the network,  whereas  the  extraction  yield  is  considere...

Our study is about feed-forward neural network’s learning method. Generally, the method of improving its learning is focused on learning rate and moment term. We focus on sigmoid functions. Sigmoid functions are used for converting input signal into output signal and adjusting connection weight of learning in feed-forward neural network. We change gradient of sigmoid functions and investigate o...