ARTIFICIAL NEURAL NETWORKS APPLICATION TO PREDICTION OF ELECTRICITY CONSUMPTION

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

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Fundamental Categories of Artificial Neural Networks

10.4018/978-1-6684-2408-7.ch001 ◽

2022 ◽

pp. 1-30

Author(s):

Arunaben Prahladbhai Gurjar ◽

Shitalben Bhagubhai Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Mathematical Operation ◽

Artificial Neural ◽

Classification Prediction ◽

Artificial Neural Network Ann ◽

Fundamental Categories

The new era of the world uses artificial intelligence (AI) and machine learning. The combination of AI and machine learning is called artificial neural network (ANN). Artificial neural network can be used as hardware or software-based components. Different topology and learning algorithms are used in artificial neural networks. Artificial neural network works similarly to the functionality of the human nervous system. ANN is working as a nonlinear computing model based on activities performed by human brain such as classification, prediction, decision making, visualization just by considering previous experience. ANN is used to solve complex, hard-to-manage problems by accruing knowledge about the environment. There are different types of artificial neural networks available in machine learning. All types of artificial neural networks work based of mathematical operation and require a set of parameters to get results. This chapter gives overview on the various types of neural networks like feed forward, recurrent, feedback, classification-predication.

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A neural network model for predicting maximum shear capacity of concrete beams without transverse reinforcement

Canadian Journal of Civil Engineering ◽

10.1139/l05-003 ◽

2005 ◽

Vol 32 (4) ◽

pp. 644-657 ◽

Cited By ~ 8

Author(s):

Ayman Ahmed Seleemah

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Experimental Work ◽

Concrete Beams ◽

Shear Capacity ◽

Transverse Reinforcement ◽

Artificial Neural ◽

Artificial Neural Network Ann

Different relationships have been proposed by codes and researchers for predicting the shear capacity of members without transverse reinforcement. In this paper, the applicability of the artificial neural network (ANN) technique as an analytical alternative to existing methods for predicting this shear capacity is investigated using a critically reviewed and agreed upon database of experimental work that serves as a basis of comparison and (or) assessment of existing and new relationships. Both ANN and eight different codes and researcher's predictions of the shear capacity of the specimens of the database were compared. The ANN predictions are much superior to those of any of the current available relationships.Key words: artificial neural networks, shear capacity, transverse reinforcement, beams.

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PEMODELAN KEBUTUHAN GIZI PADA BALITA DENGAN ARTIFICIAL NEURAL NETWORK

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2021.13.1.4363 ◽

2021 ◽

Vol 13 (1) ◽

pp. 49

Author(s):

Jajang Jajang ◽

Sylvia Meilani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Ann Model ◽

Children Under Five ◽

Under Five ◽

Nutritional Needs ◽

Artificial Neural

An information on the data is not easily obtained if the data is not presented well. The nutritional needs of toddlers are important to be studied to get an information about growth of these toddlers. This research studied the nutritional needs and its factors by using Artificial neural networks (ANN) model. These factors are toddler's age, gender, weight, height, activity. Nutrition data for children under five years old was obtained from interviews at 47 Posyandu locations. The results showed that the resulting ANN model gave quite good results where the error value generated was 0.024

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The implementation of artificial neural networks for the multivariable optimization of mesoporous NiO nanocrystalline: biodiesel application

RSC Advances ◽

10.1039/d0ra00892c ◽

2020 ◽

Vol 10 (22) ◽

pp. 13302-13315

Author(s):

Soroush Soltani ◽

Taha Roodbar Shojaei ◽

Nasrin Khanian ◽

Thomas Shean Yaw Choong ◽

Umer Rashid ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Specific Surface ◽

Relative Importance ◽

Surface Areas ◽

Specific Surface Areas ◽

Artificial Neural ◽

Artificial Neural Network Ann

In the present research, artificial neural network (ANN) modelling was utilized to determine the relative importance of effective variables to achieve optimum specific surface areas of a synthesized catalyst.

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Effective parameters on conductivity of mineralized carbon nanofibers: an investigation using artificial neural networks

RSC Advances ◽

10.1039/c6ra21596c ◽

2016 ◽

Vol 6 (113) ◽

pp. 111908-111918 ◽

Cited By ~ 21

Author(s):

Hadi Samadian ◽

Seyed Salman Zakariaee ◽

Mahdi Adabi ◽

Hamid Mobasheri ◽

Mahmoud Azami ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Carbon Nanofibers ◽

Effective Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann

The aim of this study was to predict the effects of different parameters on the conductivity of mineralized PAN-based carbon nanofibers by the artificial neural network (ANN) method.

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Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽

10.3390/s21010047 ◽

2020 ◽

Vol 21 (1) ◽

pp. 47

Author(s):

Vasyl Teslyuk ◽

Artem Kazarian ◽

Natalia Kryvinska ◽

Ivan Tsmots

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Input Data ◽

Optimization Criterion ◽

Fuzzy Input ◽

Different Types ◽

Smart House ◽

Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

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Modeling of lime production process using artificial neural network

Chemical Product and Process Modeling ◽

10.1515/cppm-2021-0032 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Abolghasem Daeichian ◽

Rana Shahramfar ◽

Elham Heidari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Production Process ◽

High Energy ◽

Ann Model ◽

Artificial Neural ◽

Rotary Kilns ◽

Input Variables ◽

Artificial Neural Network Ann ◽

Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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