Production of several yeast products occur in presence of mixtures of monosaccharides. To study effect of xylose and glucose mixtures with system aeration and nitrogen source as the other two operative variables on xylitol production by Pichia guilliermondii, the present work was defined. Artificial Neural Network (ANN) strategy was used to athematically show interplay between these three c...

Recent results changed essentially our view concerning the generality of neural networks' models. Presently, we know that such models i) are more powerful than Turing machines if they have an infinite number of neurons, ii) are universal approximators, iii) can represent any logical function, iv) can solve efficiently instances of NP-complete problems. In a previous paper [1], we discussed the ...

Algorithm Embeded with Ridge Regression Xuefeng Yan Weixiang Zhao 1(Automation Institute, College of Information Science and Engineering, East China University of Science and Technology, MeiLong Road 130, Shanghai 200237, P. R. China) 2(Department of Chemical Engineering and Center for Air Resources Engineering and Science, Clarkson University, Potsdam, NY 13699-5708, USA) Abstract Considering ...

Groundwater quality management is the most important issue in many arid and semi-arid countries, including Iran.Artificial neural network (ANN) has an extensive range of applications in water resources management. In this study,artificial neural network was developed using MATLAB R2013 software package, and Cl, EC, SO4 and NO3 qualitativeparameters were estimated and compared with the measured ...

In this study, a three–layer artificial neural network (ANN) model was developed to predict the pressure gradient in horizontal liquid–liquid separated flow. A total of 455 data points were collected from 13 data sources to develop the ANN model. Superficial velocities, viscosity ratio and density ratio of oil to water, and roughness and inner diameter of pipe were used as input parameters of ...

We propose a spiking network model capable of performing both approximate inference and learning for any hidden Markov model. The lower layer sensory neurons detect noisy measurements of hidden world states. The higher layer neurons with recurrent connections infer a posterior distribution over world states from spike trains generated by sensory neurons. We show how such a neuronal network with...