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...

In this article, we propose a technique called Energy Mixture Model (emm) for classification. emm is a type of feed-forward neural network that can be used to decide the number of nodes for constructing the hidden layer of neural networks based on the variable clustering method. Additionally, energy combination method is used to generate the recognition pattern as the basis for classification. ...

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 mode...

We validate the IEEE 802.11 MAC layer model in ns3 by means of measurements on the EXTREME testbed. We consider different scenarios: communications within a single pair of nodes, multi-user communications using either VoIP or saturated traffic, and communications in the presence of hidden nodes. For each scenario we describe in detail our testbed and simulation setup, and compare the results pr...

more than 30% of iran's land is formed from mountainous areas. so each year, landslides cause damages to structures, residential areas and forests, creating sedimentation, muddy floods and finally deposit the sediments in reservoir dams. therefore, for preventing of this damages and expressing the sensitivity rate of hillslopes, landslide hazard zonation is considered in prone areas. the p...

In this paper, we propose a novel approach for evolving the architecture of a multi-layer neural network. Our method uses combined ART1 algorithm and Max-Min neural network to self-generate nodes in the hidden layer. We have applied the proposed method to the problem of recognizing ID number in identity cards. Experimental results with a real database show that the proposed method has better pe...

We consider the problem of efficient approximate learning by multilayered feedforward circuits subject to two objective functions. First, we consider the objective to maximize the ratio of correctly classified points compared to the training set size (e.g., see [3, 5]). We show that for single hidden layer threshold circuits with n hidden nodes and varying input dimension, approximation of this...

In this paper, we present a novel Deep Triphone Embedding (DTE) representation derived from Deep Neural Network (DNN) to encapsulate the discriminative information present in the adjoining speech frames. DTEs are generated using a four hidden layer DNN with 3000 nodes in each hidden layer at the first-stage. This DNN is trained with the tied-triphone classification accuracy as an optimization c...