Statistical modeling technique has pivotal role in better understanding of the software development processes. Among them neural network techniques have enhanced predictive capability than most other statistical models. This paper explains the application of principal component analysis to neural network modeling as a way to improve predictability of neural network. The purpose of principal com...

We present a theoretical and computational work, aiming at the estimation of firing rate based excitatory inhibitory neural network from realistic stimulus-response data. The stimulus response recordings are taken previous study which performs measurement on H1 neurons order Diptera flies. parameter is performed by maximum likelihood method. As data single recording 20 minutes, it segmented ind...

sloshing is a well-known phenomenon in liquid storage tanks subjected to base or body motions. in recent years the use of multiple vertical baffles for reducing the sloshing effects in tanks subjected to earthquake has not been taken into consideration so much. on the other hand, although some of the existing computer programs are capable to model sloshing phenomenon with acceptable accuracy, t...

Multi-layered neural networks have recently been proposed for nonlinear prediction and system modeling. Although proven successful for modeling time invariant nonlinear systems, the inability of neural networks to characterize temporal variability has so far been an obstacle in applying them to complicated non stationary signals, such as speech. In this paper we present a network architecture, ...