HARDWARE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS USING BACK PROPAGATION ALGORITHM ON FPGA

Abstract Predicting rainfall can prevent and mitigate damages caused by its deficit or excess, besides providing necessary tools for adequate planning for the use of water. This research aimed to predict the monthly rainfall, one month in advance, in four municipalities in the metropolitan region of Belo Horizonte, using artificial neural networks (ANN) trained with different climate variables, and to indicate the suitability of such variables as inputs to these models. The models were developed through the MATLAB® software version R2011a, using the NNTOOL toolbox. The ANN’s were trained by the multilayer perceptron architecture and the Feedforward and Back propagation algorithm, using two combinations of input data were used, with 2 and 6 variables, and one combination of input data with 3 of the 6 variables most correlated to observed rainfall from 1970 to 1999, to predict the rainfall from 2000 to 2009. The most correlated variables to the rainfall of the following month are the sequential number corresponding to the month, total rainfall and average compensated temperature, and the best performance was obtained with these variables. Furthermore, it was concluded that the performance of the models was satisfactory; however, they presented limitations for predicting months with high rainfall.

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Hybrid Electric Vehicle Emissions Monitoring and Estimation using Artificial Neural Networks: Technical Note

International Journal of Vehicle Structures and Systems ◽

10.4273/ijvss.11.3.06 ◽

2019 ◽

Vol 11 (3) ◽

Author(s):

K. Sujatha ◽

V. Karthikeyan ◽

V. Balaji ◽

N.P.G. Bhavani ◽

V. Srividhya ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Performance Indicators ◽

Hybrid Electric Vehicle ◽

Back Propagation ◽

Technical Note ◽

Back Propagation Algorithm ◽

Highway Traffic ◽

Artificial Neural ◽

Optimal Control Action

Power is utilized as the prime fuel for hybrid and module electric vehicles in order to build the productivity of commercial vehicles. This paper forecasts the emission factors utilizing discrete Fourier transform, artificial neural networks and hybridization of back propagation algorithm. The DFT facilitates the extraction of the performance indicators which are otherwise called the features. The coefficients of the power spectrum denote the performance indicators. The ANN learns the pattern for emissions from HEVs using these performance indicators. This ANN based strategy offers an optimal control action to detect and reduce the exhaust gas emissions which are hazardous. These vehicles are provided with automated highway traffic Jam assist. Hence the forecast of these emissions offers increased efficiency of 90% to 100% thereby ensuring optimal operating condition for the hybrid vehicles.

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Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

Archives of Environmental Protection ◽

10.1515/aep-2017-0034 ◽

2017 ◽

Vol 43 (4) ◽

pp. 26-32 ◽

Cited By ~ 2

Author(s):

Sinan Mehmet Turp

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Back Propagation ◽

Data Sets ◽

Adsorption Efficiency ◽

Back Propagation Algorithm ◽

Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

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Identification and Classification of Animal Kingdom using Image Processing and Artificial Neural Networks

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6840.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 4645-4650

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Back Propagation ◽

Bilateral Symmetry ◽

Automated System ◽

Back Propagation Algorithm ◽

Animal Kingdom ◽

Artificial Neural ◽

Eukaryotic Organisms

The biological kingdom ‘Animalia’ is composed of multi cellular eukaryotic organisms. Most of the animal species exhibit bilateral symmetry. The hierarchy of biological classification has eight taxonomy ranks. The top position in the hierarchy is occupied by the ‘domain’ and ending with the lowest position occupied by ‘species’. The classification of animal kingdom includes, Porifera, Coelenterata, Platyhelminthes, Aschelminthes, Annelida, Arthropoda, Mollusca, Echinodermata and Chordata. Manual identification of Phylum or class for each and every species, is very tedious, because there exists nearly a millions of species categorized under various classes. Hence an automated system is proposed to be developed using image segmentation and Artificial Neural Networks (ANN) trained with Back Propagation Algorithm (BPA) which is capable of assisting the scientists and researchers for class identification. This system will be useful in Museums and Archeological departments, where a huge variety of species are maintained. The classification efficiency of the proposed system is 89.1%.

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