scholarly journals Artificial Neural Network Based Efficiency Prediction and Its Impact on Dye Synthesized Solar Cell

2020 ◽  
Vol 8 (5) ◽  
pp. 2696-2699
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Cell ◽  
Artificial Neural Network Model ◽  
Neural System ◽  
Natural Conditions ◽  
Forward Algorithm ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Efficiency Data

Alongside numerous different parameters, the general proficiency of pv element relies upon temperature of cell, which, depends on different ecological variables. Natural conditions, for instance, speed of wind solar irradiance, and wind course and in particular, the temperature around the cell influences cells exhibition. Also the climate forecast and meteorology is an exceptionally perplexing and loose science, ongoing exploration exercises with counterfeit neural system (ANN) have indicated that it has ground-breaking design arrangement and example acknowledgment capacities and that can be utilized as a device to obtain a sensible precise expectation of climate designs. This paper focuses on an application of Artificial Neural Network (ANN) to estimate the efficiency of Dye Synthesized solar cell. During writing of this research paper, development last ten years has been considered. An Artificial Neural Network model based on Multilayer Perceptron concept has been developed and trained using Levenberg-Marquardt feed-forward algorithm for prediction. The model was tested and trained using ten years of efficiency data of Dye synthesized solar cell. The exactness of the model was determined on premise of Mean Square Error. The result shows that Neural Network can be used for efficiency prediction successfully

scholarly journals Artificial Neural Network Based shafts surface pressures Analysis

2021 ◽  
Vol 2089 (1) ◽  
pp. 012046
Author(s):  
B V Ramana Murthy ◽  
Vuppu Padmakar ◽  
B N S M Chandrika ◽  
Satya Prasad Lanka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Neural System ◽  
Scientific Model ◽  
System Models ◽  
Learning Rules ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Forecast Quality

Abstract This paper exhibits a development of an Artificial Neural Network (ANN) as an instrument for investigation of various parameters of a framework. ANN comprises of various layers of straightforward handling components called as neurons. The neuron performs two capacities, to be specific, assortment of sources of info and age of a yield. Utilization of ANN gives diagram of the hypothesis, learning rules, and uses of the most significant neural system models, definitions and style of Computation. The scientific model of system illuminates the idea of sources of info, loads, adding capacity, actuation work and yields. At that point ANN chooses the sort of learning for modification of loads with change in parameters. At long last the examination of a framework is finished by ANN execution and ANN preparing and forecast quality.

scholarly journals Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

2019 ◽  
Vol 20 (2) ◽  
pp. 152
Author(s):  
Indra Cahyadi ◽  
Heri Awalul Ilhamsah ◽  
Ika Deefi Anna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Factorial Design ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Model Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura.  The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

Predicting the optimum compositions of a parenteral nanoemulsion system loaded with azithromycin antibiotic utilizing the artificial neural network model

RSC Advances ◽  
2015 ◽  
Vol 5 (101) ◽  
pp. 82654-82665 ◽  
Author(s):  
Ghaidaa S. Daood ◽  
Hamidon Basri ◽  
Johnson Stanslas ◽  
Hamid Reza Fard Masoumi ◽  
Mahiran Basri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Statistical Technique ◽  
Brain Delivery ◽  
Multivariate Statistical ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

For the purpose of brain delivery via intravenous administration, the formulation of an azithromycin-loaded nanoemulsion system was optimized utilizing the artificial neural network (ANN) as a multivariate statistical technique.

An Artificial Neural Network Model to Predict Material Characteristics From the Results of Miniature Disk Bending Tests

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
A. K. Ghosh ◽  
Vishnu Verma ◽  
G. Behera
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Inverse Problem ◽  
Artificial Neural Network ◽  
Material Properties ◽  
Artificial Neural Network Model ◽  
Large Database ◽  
Bending Tests ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The inverse problem of evaluating mechanical properties of material from the observed values of load and deflection of a miniature disk bending specimen is discussed in this paper. It involves analysis of large amplitude, elasto-plastic deformation considering contact and friction. The approach in this work is to first generate—by a finite element (FE) solution—a large database of load-displacement (P-w) records for varying material properties. An artificial neural network (ANN) is trained with some of these data. The errors in the various values of the parameters during testing with additional known data were found to be reasonably small.

Optimization of the Culture Medium for Rhodospirillum rubrum S1 with an Artificial Neural Network Model and GA

2013 ◽  
Vol 756-759 ◽  
pp. 172-175
Author(s):  
Yan Pan ◽  
Wei Jian Wang ◽  
Hai Juan Tian ◽  
Shi Hao Li ◽  
Zhu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Artificial Neural Network Model ◽  
Culture Medium ◽  
Rhodospirillum Rubrum ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Central Composite

on the basis of the data from the previous box-behnken central composite design, an Artificial Neural Network (ANN) model was constructed for the prediction of outputs of carotenoids. GA (genetic algorithm) was used to search for the optimal culture medium for Rhodospirillum Rubrum S1:citric acid 3.678g/L, Beef extract 3.407 g/L, MgSO4 0.524g/L, FeSO40.023g/L. In the optimal culture medium, it was predicted that the outputs of the carotenoids were13.85 mg/ L.After three verification experiments, the outputs of the carotenoids were 13.72mg/L, the error between the expected value and the experimental value was 0.93%.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2022 ◽  
pp. 427-439
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

Role of Artificial Neural Network for Prediction of Gait Parameters and Patterns

2020 ◽  
pp. 124-135
Author(s):  
Kamalpreet Sandhu ◽  
Vikram Kumar Kamboj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Human Locomotion ◽  
Neural System ◽  
Learning Ability ◽  
Ann Model ◽  
Gait Parameters ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Walking is very important exercise. Walking is characterized by gait. Gait defines the bipedal and forward propulsion of center of gravity of the human body. This chapter describes the role of artificial neural network (ANN) for prediction of gait parameters and patterns for human locomotion. The artificial neural network is a mathematical model. It is computational system inspired by the structure, processing method, and learning ability of a biological brain. According to bio-mechanics perspective, the neural system is utilized to check the non-direct connections between datasets. Also, ANN model in gait application is more desired than bio-mechanics strategies or statistical methods. It produces models of gait patterns, predicts horizontal ground reactions forces (GRF), vertical GRF, recognizes examples of stand, and predicts incline speed and distance of walking.

scholarly journals Development of Artificial Neural Network Model for Estimation of Salt Fields Productivity

2019 ◽  
Vol 20 (2) ◽  
pp. 48
Author(s):  
Indra Cahyadi ◽  
Heri Awalul Ilhamsah ◽  
Ika Deefi Anna
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Speed ◽  
Prediction Model ◽  
Factorial Design ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Model Performance ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, Indonesia needs import million tons of salt to satisfy domestic industries demand. The production of salt in Indonesia is highly dependent on the weather. Therefore, this article aims to develop a prediction model by examining rainfall, humidity and wind speed data to estimate salt production. In this research, Artificial Neural Network (ANN) method is used to develop a model based on data collected from Kaliumenet Sumenep Madura.  The model analysis uses the full experimental factorial design to determine the effect of the ANN parameter differences. Then, the selected model performance compared with the estimate predictor of Holt-Winters. The results present that ANN-based models are more accurate and efficient for predicting salt field productivity.

Sign in / Sign up

Export Citation Format

Share Document