results of damage prediction in buildings can be used as a useful tool for managing and decreasing seismic risk of earthquakes. in this study, damage spectrum and c4.5 decision tree algorithm were utilized for damage prediction in steel buildings during earthquakes. in order to prepare the damage spectrum, steel buildings were modeled as a single-degree-of-freedom (sdof) system and time-history...

Deep networks have been widely used in many domains in recent years. However, the pre-training of deep networks is time consuming with greedy layer-wise algorithm, and the scalability of this algorithm is greatly restricted by its inherently sequential nature where only one hidden layer can be trained at one time. In order to speed up the training of deep networks, this paper mainly focuses on ...

Acoustic model training is very important in speech recognition. But in traditional training algorithm, we take each state separately, and the relationship between different states is not considered. In this paper we bring forward a novel idea of using the correlation information between states, which is called “spatial correlation”. We describe this correlation information as linear constraint...

In this paper, we describe an application of the Forward-Backwards (F-B) algorithm for maximum likelihood training of hybrid HMM/Bayesian Network (BN) acoustic models. Previously, HMM/BN parameter estimation was based on a Viterbi training algorithm that requires two passes over the training data: one for BN learning and one for updating HMM transition probabilities. In this work, we first anal...

BP Neural Network has a longer training time and a slow convergence. To deal with the defects of BP Neural Network a modified BP algorithm is proposed in the paper. The algorithm is applied for the control of Inverted Pendulum, a highly non linear system inherently being open loop unstable. Levenberg-Marquardt algorithm is used for the training purpose. The training samples are being collected ...

The large amount of computation necessary for obtaining time optimal solution for moving a manipulator on specified path has made it impossible to introduce an on line time optimal control algorithm. Most of this computational burden is due to calculation of switching points. In this paper a learning algorithm is proposed for finding the switching points. The method, which can be used for both ...