Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...

Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural netw...

This paper proves that supervised learning algorithms used to train recurrent neural networks have an equilibrium point when the network implements a Maximum A Posteriori Probability (MAP) classiier. The result holds as a limit when the size of the training set goes to innnity. The result is general, since it stems as a property of cost minimizing algorithms, but to prove it we implicitly assum...

Label propagation is a powerful and flexible semi-supervised learning technique on graphs. Neural network architectures, on the other hand, have proven track records in many supervised learning tasks. In this work, we propose a training objective for neural networks, Neural Graph Machines, for combining the power of neural networks and label propagation. The new objective allows the neural netw...

In this paper, we propose a novel neural architecture that adaptively learns an input-output mapping using both supervised and non-supervised trainings. This neural architecture consists of a combination of an ART2 (Adaptive Resonance Theory) neural network and recurrent neural networks. For this end, we developed an Extended Kalman Filter (EKF) based training algorithm for the involved recurre...

A novel face recognition method based on multi-features using a neural networks committee (NNC) machine is proposed in this paper. The committee consists of several independent neural networks trained by different image blocks of the original images in different feature domains. The final classification results represent a combined response of the individual networks. Then, we use the designed ...

Random Neural Networks (RNNs) are a class of Neural Networks (NNs) that can also be seen as a specific type of queuing network. They have been successfully used in several domains during the last 25 years, as queuing networks to analyze the performance of resource sharing in many engineering areas, as learning tools and in combinatorial optimization, where they are seen as neural systems, and a...

porosity is one of the most important properties for comprehensive studies of hydrocarbon reservoirs. for determination of porosity in a rock, that is the ratio of volume of voids to the total volume of the rock, there are two conventional methods: in the first method, direct measurement of porosity is carried out by testing drilling cores. in the second method, porosity is determined indirectl...

production of highly viscous tar sand bitumen using steam assisted gravity drainage (sagd) with a pair of horizontal wells has advantages over conventional steam flooding. this paper explores the use of artificial neural networks (anns) as an alternative to the traditional sagd simulation approach. feed forward, multi-layered neural network meta-models are trained through the back-error-propaga...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...