Training a Feed-forward Neural Network with Artificial Bee Colony Based Backpropagation Method

Back-propagation algorithm is one of the most widely used and popular techniques to optimize the feed forward neural network training. Nature inspired meta-heuristic algorithms also provide derivative-free solution to optimize complex problem. Artificial bee colony algorithm is a nature inspired meta-heuristic algorithm, mimicking the foraging or food source searching behaviour of bees in a bee colony and this algorithm is implemented in several applications for an improved optimized outcome. The proposed method in this paper includes an improved artificial bee colony algorithm based back-propagation neural network training method for fast and improved convergence rate of the hybrid neural network learning method. The result is analysed with the genetic algorithm based back-propagation method, and it is another hybridized procedure of its kind. Analysis is performed over standard data sets, reflecting the light of efficiency of proposed method in terms of convergence speed and rate.Comment: 14 Pages, 11 figure

An Improved Gauss-Newtons Method based Back-propagation Algorithm for Fast Convergence

The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The efficacy of the proposed method is observed during the training period as it converges quickly for the dataset used in test. The requirement of memory for computing the steps of algorithm is also analyzed.Comment: 7 pages, 6 figures,2 tables, Published with International Journal of Computer Applications (IJCA

Effect of particle size on thermal conductivity of nanofluid

Nanofluids, containing nanometric metallic or oxide particles, exhibit extraordinarily high thermal conductivity. It is reported that the identity (composition), amount (volume percent), size, and shape of nanoparticles largely determine the extent of this enhancement. In the present study, we have experimentally investigated the impact of Al2Cu and Ag2Al nanoparticle size and volume fraction on the effective thermal conductivity of water and ethylene glycol based nanofluid prepared by a two-stage process comprising mechanical alloying of appropriate Al-Cu and Al-Ag elemental powder blend followed by dispersing these nanoparticles (1 to 2 vol pct) in water and ethylene glycol with different particle sizes. The thermal conductivity ratio of nanofluid, measured using an indigenously developed thermal comparator device, shows a significant increase of up to 100 pct with only 1.5 vol pct nanoparticles of 30- to 40-nm average diameter. Furthermore, an analytical model shows that the interfacial layer significantly influences the effective thermal conductivity ratio of nanofluid for the comparable amount of nanoparticles

Structural and Electrical Characterization of Porous Silicon Carbide Formed in n-6H-SiC Substrates

Investigation of porous silicon carbide layer morphology and its growth rate was studied along with electrical characterization. Morphology of the formed porous SiC layers was analyzed by scanning electron microscopy. The effective carrier density in porous layers was extracted from the capacitance-voltage characteristics of mercury probe Schottky contacts to the porous layer. It was found that the effective carrier density in porous layer and the pore density are in good correlation. The wide bandgap of silicon carbide ͑SiC͒ semiconductor gives it the edge over other materials for making high power, high temperature, and high frequency devices. High thermal conductivity, saturation electric drift velocity, and breakdown electric field adds to its better thermal and electronic properties. In the last few years it has been recognized that nanostructured porous semiconductor networks show interesting optoelectrical properties different from those of bulk semiconductors. These properties are related to the presence of a three-dimensional ͑3-D͒ interfacial structure with a huge internal surface area and huge volume density of surface-localized electrons. At present, extensive research is devoted to nanostructured semiconductor networks. It is believed that such networks will play an important role in future ͑opto-͒ electronic devices ͑solar cells, light emitting diodes, chemical sensors, electrochromic devices, single electron transistors͒. In recent years, porous silicon carbide has been of interest due to its more efficient luminescence compared to bulk SiC. 1 Also, electroluminescent and gas sensor devices based on porous SiC have been demonstrated. 2,3 In order to use porous SiC in device application, the correlation between electrical characteristics and structural morphology of the porous layer must be understood. The goal of this work was to investigate the surface and pore morphology of 6H-SiC with respect to the effect of varying current density used during electrochemical anodization. The characterization technique used to study the surface and pore morphology has never been reported before. Preparation and Characterization Porous silicon carbide ͑por-SiC͒ samples were prepared using n-6H-SiC (0°8Ј off axis͒ wafers from CREE Research Inc. This wafer was nitrogen doped and had a resistivity of 0.174 ⍀ cm. Photo-assisted electrochemical etching was performed on both the polished silicon-and carbon-terminated faces of the samples using a 150 W mercury ͑Hg͒ lamp and a mixture of hydrofluoric acid ͑HF͒ ͑1͒: ethanol ͑1͒ as electrolyte for a time period of 2-60 min. Prior to turning on the current, the sample arrangement in the Teflon cell was kept under the Hg lamp for 1 min. The counter electrode was a platinum wire positioned about 1 cm from the sample. The applied current density was between 10 and 80 mA/cm 2 . Por-SiC samples were analyzed after ultrasonic cleaning in methanol for 10-20 min. Thicknesses of the porous layers were measured by the cylindrical grove technique. In order to study the porous structure beneath the surface, some samples were subjected to dry etching by reactive ion etching ͑RIE͒ to remove a thin ͑0.1-0.3 m͒ surface layer. The RIE was performed in the March Instrument, Inc. system using a gas mixture (CF 4 ϩ 15%O 2 ) at 150 W. A scanning electron microscope ͑SEM͒ was used to study the microstructure of the porous SiC surface layer. In this work we have taken two batches of five samples each, and we are showing the recurring results for SEM images. For the plots, average results of both the experiments are considered. Results and Discussion 5 This explains the formation of thinner porous layer on the Si face compared to the C face at the same conditions. The anodization rate appeared to be directly related to the current density used in these experiments. For the initial period ͑up to 10 min͒, the growth rate increases from 0.3 to 1.1 m/min for the Si face and from 0.8 to 1.5 m/min for the C face when the current