neural network is one of the most widely used algorithms in the field of machine learning, on the other hand, neural network training is a complicated and important process. supervised learning needs to be organized to reach the goal as soon as possible. a supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.  hen...

A key task for speech recognition systems is to reduce the mismatch between training and evaluation data that often attributable speaker differences. Speaker adaptation techniques play a vital role mismatch. Model-based approaches require sufficient amounts of target ensure robustness. When amount level limited, prone overfitting poor generalization. To address issue, this paper proposes full B...

We describe various industrial and business applications of a common approach for learning the probability distributions and the structure of Bayesian networks. After describing the Bayesian learning theory, we explain how the parameters learned with this method can be used for prediction tasks in various application domains. In the first domain we learn the structure and the probability distri...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...

Awareness of the level of river flow and its fluctuations at different times is one of the significant factor to achieve sustainable development for water resource issues. Therefore, the present study two hybrid models, Wavelet- Adaptive Neural Fuzzy Interference System (WANFIS) and Wavelet- Artificial Neural Network (WANN) are used for flow prediction of Gamasyab River (Nahavand, Hamedan, Iran...

Radial Basis Neural (RBN) network has the power of the universal approximation function and the convergence of those networks is very fast compared to multilayer feedforward neural networks. However, how to determine the architecture of the RBN networks to solve a given problem is not straightforward. In addition, the number of hidden units allocated in an RBN network seems to be a critical fac...

We present a new method to approximate posterior probabilities of Bayesian Network using Deep Neural Network. Experiment results on several public Bayesian Network datasets shows that Deep Neural Network is capable of learning joint probability distribution of Bayesian Network by learning from a few observation and posterior probability distribution pairs with high accuracy. Compared with tradi...