Application of artificial neural networks in the process of drying CPC

Training of artificial neural networks using the experimental data obtained in the process of drying of cotton powdered cellulose (CPC), have shown that, experiments can be conducted on the computers, which solves many of the practical and ethical issues. Therefore, arose and remain at the present time, the objective of neural simulation of processes and create a computing system (artificial neural network)

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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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FORECASTING THE INTENSITY OF SOLAR RADIATION BASED ON ARTIFICIAL NEURAL NETWORKS

Thermophysics and Thermal Power Engineering ◽

10.31472/ttpe.2.2021.7 ◽

2021 ◽

Vol 43 (2) ◽

pp. 60-67

Author(s):

B.I. Basok ◽

M.P. Novitska ◽

V.P. Kravchenko

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Solar Radiation ◽

Data Set ◽

Advantages And Disadvantages ◽

Database Evaluation ◽

Artificial Neural

The paper considers short-term forecasting of the intensity of solar radiation in the city of Odessa based on an artificial neural network. The artificial neural network was trained on the experimental data of the ground weather station (Davis 6162EU), which is installed on the roof of the educational building of the Odessa National Polytechnic University. Modeling, validation, and testing of experimental data were performed using the MATLAB software package, namely Neural Network Toolbox. The Levenberg-Markwatt model is used in this work. The analyzed data set was divided into proportions of 70%, 15%, 15% for neural network training, its validation, and testing, respectively. The results which the trained neural network gave during forecasting within the framework of the database and outside it are given. The deviation between real and forecast data is analyzed. The root-mean-square error on December 26, 2016 was 13.03 W / m2, and on December 27, 2016 - 9.44 W / m2 when forecasting outside the database. Evaluation of the accuracy of an artificial neural network has shown its effectiveness in predicting the intensity of solar radiation. To predict parameters based on artificial neural networks, experimental data that describe a real system are needed. Artificial neural networks, like other approximation methods, have both advantages and disadvantages.

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Enhancement of Performance for Steam Turbine in Thermal Power Plants Using Artificial Neural Network and Electric Circuit Design

Applied Computational Intelligence and Soft Computing ◽

10.1155/2018/8042498 ◽

2018 ◽

Vol 2018 ◽

pp. 1-9

Author(s):

Hosham Salim ◽

Khalid Faisal ◽

Raheel Jawad

Keyword(s):

Neural Network ◽

Experimental Data ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Power Plant ◽

Circuit Design ◽

Electric Circuit ◽

Maximum Deviation ◽

Artificial Neural

Design and implantation of electric circuit for enhanced performance of steam power plant and artificial neural networks technique are used to control turbine. Artificial neural networks technique is used to control a lot of industrial models practically. Artificial neural network has been applied to control the important variables of turbine in AL–Dura power plant in Baghdad such as pressure, temperature, speed, and humidity. In this study Simulink model was applied in MATLAB program (v 2014 a) by using artificial neural network (ANN). The method of controlling model is by using NARMA to generate data and train network. ANN is offline. ANN requires data to obtain results and for comparison with actual power plant. The values of the input variables have a large effect on the number of nodes and epochs and in hidden layer of the artificial neural network they also affect performance of ANN. The electric circuit of sensors consists of transformer, DC bridge, and voltage regulator. Comparing the results from modeling by ANN and electric circuit with experimental data reveals a good agreement and the maximum deviation between the experimental data and predicted results from ANN and circuit design is less than 1%. The novelty in this paper is applying NARMA controller for the purpose of enhancement of turbine performance.

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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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FEATURES OF DIAGNOSTIC ARTIFICIAL NEURAL NETWORKS FOR HYBRID EXPERT SYSTEMS

Digital Technologies ◽

10.33243/2313-7010-26-36-46 ◽

2019 ◽

Vol 26 ◽

pp. 36-46

Author(s):

S. KONOVALOV ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Expert Systems ◽

Technical System ◽

Bulletin Board ◽

Confidence Factor ◽

Complex Technical System ◽

Artificial Neural

In the proposed article, various methods of constructing an artificial neural network as one of the components of a hybrid expert system for diagnosis were investigated. A review of foreign literature in recent years was conducted, where hybrid expert systems were considered as an integral part of complex technical systems in the field of security. The advantages and disadvantages of artificial neural networks are listed, and the main problems in creating hybrid expert systems for diagnostics are indicated, proving the relevance of further development of artificial neural networks for hybrid expert systems. The approaches to the analysis of natural language sentences, which are used for the work of hybrid expert systems with artificial neural networks, are considered. A bulletin board is shown, its structure and principle of operation are described. The structure of the bulletin board is divided into levels and sublevels. At sublevels, a confidence factor is applied. The dependence of the values of the confidence factor on the fulfillment of a particular condition is shown. The links between the levels and sublevels of the bulletin board are also described. As an artificial neural network architecture, the «key-threshold» model is used, the rule of neuron operation is shown. In addition, an artificial neural network has the property of training, based on the application of the penalty property, which is able to calculate depending on the accident situation. The behavior of a complex technical system, as well as its faulty states, are modeled using a model that describes the structure and behavior of a given system. To optimize the data of a complex technical system, an evolutionary algorithm is used to minimize the objective function. Solutions to the optimization problem consist of Pareto solution vectors. Optimization and training tasks are solved by using the Hopfield network. In general, a hybrid expert system is described using semantic networks, which consist of vertices and edges. The reference model of a complex technical system is stored in the knowledge base and updated during the acquisition of new knowledge. In an emergency, or about its premise, with the help of neural networks, a search is made for the cause and the control action necessary to eliminate the accident. The considered approaches, interacting with each other, can improve the operation of diagnostic artificial neural networks in the case of emergency management, showing more accurate data in a short time. In addition, the use of such a network for analyzing the state of health, as well as forecasting based on diagnostic data using the example of a complex technical system, is presented.

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An artificial neural network identifies glyphosate-impacted brackish communities based on 16S rRNA amplicon MiSeq read counts

10.1101/711309 ◽

2019 ◽

Author(s):

René Janßen ◽

Jakob Zabel ◽

Uwe von Lukas ◽

Matthias Labrenz

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Environmental Monitoring ◽

Microbial Communities ◽

List Type ◽

Neural Net ◽

Next Generation ◽

Artificial Neural

AbstractArtificial neural networks can be trained on complex data sets to detect, predict, or model specific aspects. Aim of this study was to train an artificial neural network to support environmental monitoring efforts in case of a contamination event by detecting induced changes towards the microbial communities. The neural net was trained on taxonomic cluster count tables obtained via next-generation amplicon sequencing of water column samples originating from a lab microcosm incubation experiment conducted over 140 days to determine the effects of the herbicide glyphosate on succession within brackish-water microbial communities. Glyphosate-treated assemblages were classified correctly; a subsetting approach identified the clusters primarily responsible for this, permitting the reduction of input features. This study demonstrates the potential of artificial neural networks to predict indicator species in cases of glyphosate contamination. The results could empower the development of environmental monitoring strategies with applications limited to neither glyphosate nor amplicon sequence data.Highlight bullet pointsAn artificial neural net was able to identify glyphosate-affected microbial community assemblages based on next generation sequencing dataDecision-relevant taxonomic clusters can be identified by a stochastically subsetting approachJust a fraction of present clusters is needed for classificationFiltering of input data improves classification

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A single layer artificial neural network type architecture with molecular engineered bacteria for reversible and irreversible computing

Chemical Science ◽

10.1039/d1sc01505b ◽

2021 ◽

Author(s):

Kathakali Sarkar ◽

Deepro Bonnerjee ◽

Rajkamal Srivastava ◽

Sangram Bagh

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Basic Concept ◽

Single Layer ◽

Artificial Neural ◽

Engineered Bacteria ◽

Network Type

Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible...

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Forecasting Stock Returns with Artificial Neural Networks

Neural Networks in Business Forecasting ◽

10.4018/978-1-59140-176-6.ch003 ◽

2011 ◽

pp. 47-79 ◽

Cited By ~ 5

Author(s):

Suraphan Thawornwong ◽

David Enke

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Stock Returns ◽

Future Research ◽

Network Architectures ◽

Network Applications ◽

Artificial Neural ◽

Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

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Artificial Neural Network for Solving the Inventory Control Problem in Fuzzy Environments

Artificial Neural Network Applications in Business and Engineering - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3238-6.ch006 ◽

2021 ◽

pp. 126-143

Author(s):

Mostafijur Rahaman ◽

Sankar Prasad Mondal ◽

Shariful Alam

Keyword(s):

Neural Network ◽

Differential Equation ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Inventory Control ◽

Solution Procedure ◽

Control Problems ◽

Fuzzy Environment ◽

Artificial Neural

In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role. In this chapter, different inventory control problems are formulated in fuzzy environment and solved by artificial neural network. Due to present the non-linearity associated with the differential equation in fuzzy environment, the solution procedure may be very complicated. To avoid the situation, artificial neural networks play an important role.

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Artificial Neural Networks

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch010 ◽

2011 ◽

pp. 222-243

Author(s):

Joarder Kamruzzaman ◽

Ruhul Sarker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Network Models ◽

Neural Network Models ◽

Network Applications ◽

The Neural Network ◽

Artificial Neural ◽

Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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