scholarly journals Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks

2021 ◽  
Vol 5 (2) ◽  
pp. 176-192
Author(s):  
Eko Setiawan ◽  
Dahnial Syauqy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Confusion Matrix ◽  
Sensor Data ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Balancing Robot ◽  
Artificial Neural Network Ann

A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2020 ◽  
pp. 1632-1649
Author(s):  
Veronica Chan ◽  
Christine W. Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals Implementation of Autonomous Control System of The Chain Wheel Robot Using the Backpropagation Artificial Neural Network (ANN) Methods

2020 ◽  
Vol 5 (8) ◽  
pp. 1230-1235
Author(s):  
Nur Rachman Supadmana Muda ◽  
Nugraha Gumilar ◽  
R.Djoko Andreas. Navalino ◽  
Tirton. N ◽  
M.Iman Hidayat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Robot Control ◽  
Back Propagation ◽  
Robot System ◽  
Output Data ◽  
Backpropagation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The purpose of this research is to implement the Artificial Neural Network (ANN) method in combat robots so it can be directed to shoot targets well. The robot control system uses remote control and autonomous. In the autonomous robot system, ANN back propagation method is applied, where the weight value variable depends on ultrasonic sensor, GPS and camera. The microcontroller system will process automatically depending on the sensor input. Output data is used to direct the robot to the target, tracking and shooting. Robot is used chain wheel systems and weapons that used pistol types. The riffle is mounted on the robot can be moved mechanically azimuth and the elevation towards the target then triggered mechanically by the riffle through the activation of data relays from the microcontroller. Thus, the backpropagation method can be applied to robots so it can be functioned autonomously.

Utilizing Artificial Neural Network for Real-Time Prediction of Differential Sticking Symptoms

2021 ◽  
Author(s):  
Meor M. Meor Hashim ◽  
M. Hazwan Yusoff ◽  
M. Faris Arriffin ◽  
Azlan Mohamad ◽  
Dalila Gomes ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Real Time ◽  
Performance Metrics ◽  
Confusion Matrix ◽  
Model Development ◽  
Data Sets ◽  
Proper Training ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN) approach. The model is designed to provide users, i.e., driller or monitoring specialist, a warning whenever a risk is identified. Since multi-step forecasting is used, the warning is given with enough time for the driller or monitoring specialist to evaluate which preventative action or intervention is necessary. The warnings are provided typically between 30 minutes and 4 hours ahead. The model validation includes the performance metrics and a confusion matrix. Practical cases with real-time wells are also provided. The ML model was proven robust and practical with our data sets, for both historical and live wells. The huge amount of data produced while drilling holds valuable information and when smartly fed into an Artificial Intelligence (AI) model, it can prevent NPT such as stuck pipe events as demonstrated in this paper.

scholarly journals Photodegradation of roxarsone in the aquatic environment: influencing factors, mechanisms and artificial neural network modeling

2021 ◽  
Author(s):  
Jizhong Meng ◽  
Arong Arong ◽  
Shoujun Yuan ◽  
Wei Wang ◽  
Juliang Jin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Influencing Factors ◽  
Aquatic Environment ◽  
Feed Additive ◽  
Neural Network Modeling ◽  
Factors Affecting ◽  
Indirect Photolysis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Roxarsone (ROX) is an organoarsenic feed additive, and can be discharged into aquatic environment. ROX can photodegrade into more toxic inorganic arsenics, causing arsenic pollution. However, the photodegradation behavior of ROX in aquatic environment is still unclear. To better understand ROX photodegradation behavior, this study investigated the ROX photodegradation mechanism and influencing factors, and modeled the photodegradation process. The results showed that ROX in the aquatic environment was degraded to inorganic As(III) and As(V) under light irradiation. The degradation efficiency was enhanced by 25 % with the increase of light intensity from 300 µW/cm2 to 800 µW/cm2 via indirect photolysis. The photodegradation was temperature dependence, but was only slightly affected by pH. Nitrate ion (NO3−) had an obvious influence, but sulfate, carbonate, and chlorate ions had a negligible effect on ROX degradation. Dissolved organic matter (DOM) in the solution inhibited the photodegradation. ROX photodegradation was mainly mediated by reactive oxygen species (in the form of single oxygen 1O2) generated through ROX self-sensitization under irradiation. Based on the data of factors affecting ROX photodegradation, ROX photodegradation model was built and trained by an artificial neural network (ANN), and the predicted degradation rate was in good agreement with the real values with a root mean square error of 1.008. This study improved the understanding of ROX photodegradation behavior and provided a basis for controlling the pollution from ROX photodegradation.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2017 ◽  
Vol 11 (2) ◽  
pp. 57-73 ◽  
Author(s):  
Veronica Chan ◽  
Christine Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

scholarly journals Teaching Aid For Diagnosing Motorcycle Damages Using Back Propagation Artificial Neural Network

2020 ◽  
Vol 26 (2) ◽  
pp. 140-148
Author(s):  
Nur Hasanah ◽  
Fatchul Arifin ◽  
Dessy Irmawati ◽  
Muslikhin Muslikhin ◽  
Zainal Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Teaching And Learning ◽  
Back Propagation ◽  
Bring Your Own Device ◽  
Teaching Aids ◽  
Teaching Aid ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The challenge of learning media in the world within the next 1 to 2 years is Bring Your Own Device. It forces the learning paradigm to think quickly to follow the development of technology that can optimally use it. In the Control Systems II course, there are some stereotypes that some of the material is mainly an Artificial Neural Network (ANN) was limited to theory and simulations and is difficult to be applied. Teaching aids are interpreted as teaching material that is used to help teachers in carrying out the teaching and learning activities in the classroom. The purposes of this study are: (1) to create teaching aid for ANN material to diagnose motorcycle damage in the Control System II Course (2) to define the accuracy of the application of the teaching aid for the material of ANN in the Control System II Course. The prototyping approach model is used to generally define the teaching aid product that will be developed. In detail, the development methods include (1) listen to the customer, (2) build or revise a mock-up, and (3) customer test drives mockup. Teaching aids products are built in the form of application for the diagnosis of motorcycle damages using the Back-Propagation ANN. This application can detect four types of motorcycle damages based on the sample sounds of motorcycles included. The application can recognize the type of damage from 100 new sound data outside its knowledge-base with a 60% accuracy level.

scholarly journals PERBANDINGAN METODE KLASIFIKASI ARTIFICIAL NEURAL NETWORK BACKPROPAGATION DAN REGRESI LOGISTIK (Studi Kasus : Bank Internasional Indonesia)

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Siti Hadijah Hasanah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confusion Matrix ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Kredit tanpa agunan (KTA) adalah salah satu produk kredit yang diberikan bank kepada nasabah kredit dalam bentuk fasilitas pinjaman tanpa ada suatu jaminan. Karena tidak ada jaminan atas pinjaman tersebut maka bank harus berhati-hati memeriksa calon nasabah kredit agar tidak terjadi resiko kerugian di kemudian hari. Pengajuan aplikasi KTA oleh nasabah kepada pihak bank akan dilakukan penilaian berdasarkan teknik klasifikasi. Teknik klasifikasi pada KTA ini menggunakan metode pendekatan statistik yaitu regresi logistik dan ANN. Regresi logistik merupakan salah satu metode parametrik yang tidak disyaratkan asumsi-asumsi sebagaimana yang harus dipenuhi apabila melakukan analisis data dengan menggunakan regresi linear. Metode ANN adalah pemrosesan informasi yang terinspirasi oleh sistem syaraf biologi (Haykin,1999). Metode regresi logistik memiliki kemampuan untuk  menentukan peubah penjelas yang berpengaruh  terhadap peubah respon hasil keputusan. Regresi logistik dengan peubah penjelas berpengaruh yaitu jenis kelamin, jumlah cicilan 12 bulan, jumlah cicilan 24 bulan, dan standar gaji. Jadi pihak bank dapat menjadikan peubah penjelas tersebut sebagai pertimbangan untuk menentukan hasil keputusan nasabah KTA. Berdasarkan nilai ketepatan klasifikasi confusion matrix, nilai akurasi, dan AUC pada data training dan data testing metode yang terbaik pada data nasabah KTA yaitu ANN Backpropagation diikuti oleh regresi logistik.Kata Kunci :    Kredit Tanpa Agunan, Artificial Neural Network (ANN) Backpropagation, Regresi Logistik.

scholarly journals Inventory Management of Railway Logistics Park Based on Artificial Neural Network

2020 ◽  
Vol 53 (5) ◽  
pp. 715-723
Author(s):  
Li Gao ◽  
Huandi Dou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Inventory Management ◽  
Logistics Park ◽  
Factors Affecting ◽  
Modern Information Technology ◽  
Main Factors ◽  
Modern Information ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In recent years, China has stepped up its support to the optimization and development of railways. Meanwhile, the development of modern information technology (IT) has enhanced the economic advantages of railway logistics. To intelligently manage the inventory of railway logistics park (RLP), this paper integrates artificial neural network (ANN) into RLP inventory management. Firstly, the functional demand of RLP inventory management was analyzed comprehensively, and the main factors affecting the inventory demand were divided into different categories. Then, the authors formulated the framework of intelligent inventory management for RLP, and put forward the strategy of continuous periodic inventory monitoring. Finally, a RLP inventory prediction model was constructed based on optimized genetic algorithm (GA) and backpropagation neutral network (BPNN), and proved effective through experiments. The research results provide reference for the application of ANN in inventory management and prediction in other logistics fields.

scholarly journals Neurocontrol methods in the context of development of technical solutions for transition to unmanned navigation

2021 ◽  
Vol 2061 (1) ◽  
pp. 012115
Author(s):  
A I Epikhin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control System ◽  
Electric Propulsion ◽  
Control Object ◽  
Training Sample ◽  
Surface Vessels ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Technical Solutions

Abstract The paper considers the features and prospects of using neurocontrol methods in the context of development of technical solutions for transition to unmanned merchant vessels. The paper suggests a non-iterative training based artificial neural network (ANN), which is based on the principles of “direct inverse control” to control the speed and motion of unmanned surface vessels. The model is identified, and the structure of an artificial neural network and the diagram of the automatic control system (ACS) of an unmanned vessel (UV) are considered on the example of an electric propulsion vessel. A series of computational experiments is carried out to obtain a sufficiently complete training sample. and the control law is presented. The principle of the control system for an unmanned vessel is considered based on a neural network. At the next stage of the study, focus is on the synthesis of the optimal control system for UV navigation. The problem of the fastest motion of a third-order control object from one point (with any initial speed) to another (at the end point the vessel stops and the speed is zero) is considered. Based on the results of a series of experiments with the UV model, the controller parameters that provide the best indicators of control quality were set in the MATLAB Simulink environment.

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