Deep belief network (DBN) is a probabilistic generative model with multiple layers of hidden nodes and a layer of visible nodes, where parameterizations between layers obey harmonium or restricted Boltzmann machines (RBMs). In this paper we present restricted deep belief network (RDBN) for multi-view learning, where each layer of hidden nodes is composed of view-specific and shared hidden nodes...

thermal conductivity is an important property of juices in the prediction of heat- and mass-transfer coefficients and in the design of heat- and mass-transfer equipment for the fruit juice industry. an artificial neural network (ann) was developed to predict thermal conductivity of pear juice. temperature and concentration were input variables. thermal conductivity of juices was outputs. the op...

the gross calorific value (gcv) or heating value of a sample of fuel is one of the important properties which defines the energy of the fuel. many researchers have proposed empirical formulas for estimating gcv value of coal. there are some known methods like bomb calorimeter for determining the gcv in the laboratory. but these methods are cumbersome, costly and time consuming. in this paper, m...

This study shows the usefulness of Artificial Neural Network (ANN) in maintenance planning and man-agement. An ANN model based on the multi-layer perceptron having three hidden layers and four processing elements per layer was built to predict the expected downtime resulting from a breakdown or a maintenance activity. The model achieved an accuracy of over 70% in predicting the expected downtime.

abstract erosion and sedimentation are the most complicated problems in hydrodynamic which are very important in water-related projects of arid and semi-arid basins. for this reason, the presence of suitable methods for good estimation of suspended sediment load of rivers is very valuable. solving hydrodynamic equations related to these phenomenons and access to a mathematical-conceptual model ...

Randomized weights neural networks have fast learning speed and good generalization performances with one single hidden layer structure. Input weighs of the hidden layer are produced randomly. By employing certain activation functions, outputs of the hidden layers are calculated with some randomization. Output weights are computed using pseudoinverse. Mutual information can be used to measure m...

In layered neural networks, the input space is reconstructed on the hidden layer through the connection weights from the input layer to the hidden layer and the output function of each hidden neuron. The connection weights are modi ed by learning and realize the transformation to emphasize necessary information and to degenerate unnecessary one for calculating the output. In this paper, visual ...

In this paper we propose a radial basis function (RBF) neural network for nonlinear time-invariant channel equalizer. The RBF network model has a three-layer structure which is comprised of an input layer, a hidden layer and an output layer. The learning algorithm consists of unsupervised learning and supervised learning. The unsupervised learning mainly adjusts the weight among input layer and...

In this research work, the effects of flotation parameters on coking coal flotation combustible material recovery (CMR) were studied by the artificial neural networks (ANNs) method. The input parameters of the network were the pulp solid weight content, pH, collector dosage, frother dosage, conditioning time, flotation retention time, feed ash content, and rotor rotation speed. In order to sele...