Neural networks for prediction of robot failures

2013 ◽  
Vol 228 (8) ◽  
pp. 1444-1458 ◽  
Author(s):  
Ali Diryag ◽  
Marko Mitić ◽  
Zoran Miljković
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optimal Solution ◽  
Feedforward Neural Networks ◽  
Real Data ◽  
Obstacle Detection ◽  
Network Training ◽  
Novel Approach ◽  
The Neural Network ◽  
Prediction Approach

It is known that the supervision and learning of robotic executions is not a trivial problem. Nowadays, robots must be able to tolerate and predict internal failures in order to successfully continue performing their tasks. This study presents a novel approach for prediction of robot execution failures based on neural networks. Real data consisting of robot forces and torques recorded immediately after the system failure are used for the neural network training. The multilayer feedforward neural networks are employed in order to find optimal solution for the failure prediction problem. In total, 7 learning algorithms and 24 neural architectures are implemented in two environments – Matlab and specially designed software titled BPnet. The results show that the neural networks can successfully be applied for the problem in hand with prediction rate of 95.4545%, despite having the erroneous or otherwise incomplete sensor measurements invoked in the dataset. Additionally, the real-world experiments are conducted on a mobile robot for obstacle detection and trajectory tracking problems in order to prove the robustness of the proposed prediction approach. In over 96% for the detection problem and 99% for the tracking experiments, neural network successfully predicted the failed information, which evidences the usefulness and the applicability of the developed intelligent method.

scholarly journals Rain–Induced Flood Prediction for Niger Delta Sub-Region of Nigeria Using Neural Networks

2020 ◽  
Vol 5 (9) ◽  
pp. 1124-1130
Author(s):  
Ledisi Giok Kabari ◽  
Young Claudius Mazi
Keyword(s):  
Neural Network ◽  
Climate Change ◽  
Neural Networks ◽  
Niger Delta ◽  
Feedforward Neural Networks ◽  
Flood Prediction ◽  
The Neural Network ◽  
Significant Damage ◽  
Weather Underground ◽  
Short Term Prediction

Climate change generates so many direct and indirect effects on the environment.  Some of those effects have serious consequences. Rain-induced flooding is one of the direct effects of climate change and its impact on the environment is usually devastating and worrisome. Floods are one of the most commonly occurring disasters and have caused significant damage to life, including agriculture and economy. They are usually caused in areas where there is excessive downpour and poor drainage systems. The study uses Feedforward Multilayer Neural Network to perform short-term prediction of the amount of rainfall flood for the Niger Delta   sub region of Nigeria given previous rainfall data for a specified period of time. The data for training and testing of the Neural Network was sourced from Weather Underground official web site https://www.wunderground.com.  An iterative Methodology was used and implemented in MATLAB. We adopted multi-layer Feedforward Neural Networks. The study accurately predicts the rain-induced flood for the Niger Delta   sub region of Nigeria.

scholarly journals Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl2-NH3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Onesimo Meza-Cruz ◽  
Isaac Pilatowsky ◽  
Agustín Pérez-Ramírez ◽  
Carlos Rivera-Blanco ◽  
Youness El Hamzaoui ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Heating Temperature ◽  
Barium Chloride ◽  
Symmetry Groups ◽  
Solar Cooling ◽  
Network Training ◽  
The Neural Network ◽  
Marquardt Algorithm

The aim of this work is to present a model for heat transfer, desorbed refrigerant, and pressure of an intermittent solar cooling system’s thermochemical reactor based on backpropagation neural networks and mathematical symmetry groups. In order to achieve this, a reactor was designed and built based on the reaction of BaCl2-NH3. Experimental data from this reactor were collected, where barium chloride was used as a solid absorbent and ammonia as a refrigerant. The neural network was trained using the Levenberg–Marquardt algorithm. The correlation coefficient between experimental data and data simulated by the neural network was r = 0.9957. In the neural network’s sensitivity analysis, it was found that the inputs, reactor’s heating temperature and sorption time, influence neural network’s learning by 35% and 20%, respectively. It was also found that, by applying permutations to experimental data and using multibase mathematical symmetry groups, the neural network training algorithm converges faster.

Application of neural networks in industrial production

2021 ◽  
pp. 10-17
Author(s):  
S. S. Yudachev ◽  
N. A. Gordienko ◽  
F. M. Bosy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Effective Means ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Graphics Processors ◽  
Training Scheme ◽  
Network Training ◽  
The Neural Network

The article describes an algorithm for the synthesis of neural networks for controlling the gyrostabilizer. The neural network acts as an observer of the state vector. The role of such an observer is to provide feedback to the gyrostabilizer, which is illustrated in the article. Gyrostabilizer is a gyroscopic device designed to stabilize individual objects or devices, as well as to determine the angular deviations of objects. Gyrostabilizer systems will be more widely used, as they provide an effective means of motion control with a number of significant advantages for various designs. The article deals in detail with the issue of specific stage features of classical algorithms: selecting the network architecture, training the neural network, and verifying the results of feedback control. In recent years, neural networks have become an increasingly powerful tool in scientific computing. The universal approximation theorem states that a neural network can be constructed to approximate any given continuous function with the required accuracy. The back propagation algorithm also allows effectively optimizing the parameters when training a neural network. Due to the use of graphics processors, it is possible to perform efficient calculations for scientific and engineering tasks. The article presents the optimal configuration of the neural network, such as the depth of memory, the number of layers and neurons in these layers, as well as the functions of the activation layer. In addition, it provides data on dynamic systems to improve neural network training. An optimal training scheme is also provided.

scholarly journals Solving Differential Equations Using Feedforward Neural Networks

2021 ◽  
pp. 385-399
Author(s):  
Wilson Guasti Junior ◽  
Isaac P. Santos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Differential Equations ◽  
Feedforward Neural Networks ◽  
Activation Functions ◽  
Learning Models ◽  
Python Language ◽  
The Neural Network ◽  
Minimization Methods

Abstract In this work we explore the use of deep learning models based on deep feedforward neural networks to solve ordinary and partial differential equations. The illustration of this methodology is given by solving a variety of initial and boundary value problems. The numerical results, obtained based on different feedforward neural networks structures, activation functions and minimization methods, were compared to each other and to the exact solutions. The neural network was implemented using the Python language, with the Tensorflow library.

scholarly journals Prediction of Motor Failure Time Using An Artificial Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4342 ◽  
Author(s):  
Gustavo Scalabrini Sampaio ◽  
Arnaldo Rabello de Aguiar Vallim Filho ◽  
Leilton Santos da Silva ◽  
Leandro Augusto da Silva
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Failure Time ◽  
Machine Learning Techniques ◽  
Network Training ◽  
Model Generalization ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

scholarly journals Direct adaptive control using feedforward neural networks

2003 ◽  
Vol 14 (4) ◽  
pp. 348-358 ◽  
Author(s):  
Daniel Oliveira Cajueiro ◽  
Elder Moreira Hemerly
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Feedforward Neural Networks ◽  
Control Input ◽  
Neural Adaptive Control ◽  
Direct Adaptive Control ◽  
Network Training ◽  
The Neural Network ◽  
Lyapunov’S Second Method ◽  
Kalman Filter Algorithm

This paper proposes a new scheme for direct neural adaptive control that works efficiently employing only one neural network, used for simultaneously identifying and controlling the plant. The idea behind this structure of adaptive control is to compensate the control input obtained by a conventional feedback controller. The neural network training process is carried out by using two different techniques: backpropagation and extended Kalman filter algorithm. Additionally, the convergence of the identification error is investigated by Lyapunov's second method. The performance of the proposed scheme is evaluated via simulations and a real time application.

scholarly journals Using the neural network in clinical systems

2021 ◽  
Vol 2131 (4) ◽  
pp. 042008
Author(s):  
Yu S Gusynina ◽  
T A Shornikova
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Local Level ◽  
Probabilistic Method ◽  
Complex Function ◽  
Two Dimensions ◽  
Image Size ◽  
Network Training ◽  
The Neural Network

Abstract The article examines the identification of human bone fractures using convoluted neural networks. The method of recognition of photographs of patients is intended for automated systems of identification and video recording of images. Convolutional neural networks have a number of advantages, such as invariability when reducing or increasing image size, immunity to photo movements and deviations, changes in image perspective, and many other image errors. In addition, convolutional neural networks allow you to combine neurons at a local level in two dimensions, connect photographic elements in any place, and also reduce the total number of weights. The work describes a multi-layer convolutional network. The layers of which it consists are divided into two types: convolutional and sub-selective. Of interest is the use of the principle of weighting in the work. This principle allows you to reduce the number of characteristics of the neural network that can be trained. Network training is based on the rule of minimizing empirical error. This rule is based on the algorithm of inverse error propagation. This algorithm provides an instant calculation of the gradient of a complex function of several variables in case the function itself is predefined. Neural network training is based on probabilistic method. This method leads to more optimal results due to interference in the restructuring of network weights. The work confirms the axiomatics of the applied neural network, its architecture and its learning algorithm.

scholarly journals Blender as a tool for generating synthetic data

2020 ◽  
Vol 16 ◽  
pp. 227-232
Author(s):  
Rafał Sieczka ◽  
Maciej Pańczyk
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Synthetic Data ◽  
Image Data ◽  
Real Data ◽  
Neural Network Training ◽  
Data Set ◽  
Network Training ◽  
Significant Acceleration ◽  
Graphics Software

Acquiring data for neural network training is an expensive and labour-intensive task, especially when such data isdifficult to access. This article proposes the use of 3D Blender graphics software as a tool to automatically generatesynthetic image data on the example of price labels. Using the fastai library, price label classifiers were trained ona set of synthetic data, which were compared with classifiers trained on a real data set. The comparison of the resultsshowed that it is possible to use Blender to generate synthetic data. This allows for a significant acceleration of thedata acquisition process and consequently, the learning process of neural networks.

scholarly journals Prediction of e-Learning Efficiency by Neural Networks

2012 ◽  
Vol 12 (2) ◽  
pp. 98-108 ◽  
Author(s):  
Petar Halachev
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Balanced Scorecard ◽  
Small Sample Size ◽  
Principal Component ◽  
Real Data ◽  
Training Sample ◽  
Small Sample ◽  
The Neural Network ◽  
E Learning

Abstract A model for prediction of the outcome indicators of e-Learning, based on Balanced ScoreCard (BSC) by Neural Networks (NN) is proposed. In the development of NN models the problem of a small sample size of the data arises. In order to reduce the number of variables and increase the examples of the training sample, preprocessing of the data with the help of the methods Interpolation and Principal Component Analysis (PCA) is performed. A method for optimizing the structure of the neural network is applied over linear and nonlinear neural network architectures. The highest accuracy of prognosis is obtained applying the method of Optimal Brain Damage (OBD) over the nonlinear neural network. The efficiency and applicability of the method suggested is proved by numerical experiments on the basis of real data.

SYNTHESIS OF NEURAL NETWORK BASED ON PETRY NET USING AS EXAMPLE THE PROBLEM OF MOVEMENT AND STABILIZATION OF STRUCTURE GROUPS OF UNMANNED AERIAL VEHICLES

2018 ◽  
pp. 26-33
Author(s):  
Dmitry Olegovich Romannikov ◽  
Alexander Aleksandrovich Voevoda
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Petri Nets ◽  
Unmanned Aerial Vehicles ◽  
Training Time ◽  
Initial Values ◽  
Network Training ◽  
Aerial Vehicles ◽  
The Neural Network ◽  
Parameter Values

The article focuses on the approach to forming the structure of a neural network with application of a pre-built algorithm using Petri nets which represent a well-characterized body of mathematics and help to describe algorithms, in particular, distributed asynchronous systems. According to the proposed approach, the model built in Petri nets serves as the basis for further developing the neural network. There was proposed the idea of informal transformation, which makes sense because the structure of Petri net provides substantiation for the structure of the neural network. This fact leads to decreasing the number of training parameters in the neural network (in the example described in the article the decrease was more than twice: from 650 to 254), increasing the time of the network training and getting initial values for the training parameters. It has been stated that with the initial values obtained the training time grows even more and, thus, training process acts as fine-adjusting values of parameters. Transformation can be explained by the fact that both Petri nets and neural networks act as languages for describing functions, and differ only in the case of neural networks, where the presented function must be trained first (or to find parameter values). The above-mentioned approach is illustrated by the example of the problem of automatic formation of a group of unmanned aerial vehicles (UAV) and their movement. In this problem, identical instances of the neural network are located on each UAV and interact in asynchronous mode.

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