scholarly journals Development of Smart Number Writing Robotic Arm using Stochastic Gradient Decent Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 542-547
Keyword(s):  
Neural Network ◽  
Research Work ◽  
Input Image ◽  
Stochastic Gradient ◽  
Robotic Systems ◽  
Robotic Arm ◽  
Robot Arm ◽  
Smart Systems ◽  
The Neural Network ◽  
Visual Aid

Robotics and Neural Networks will play a major role in the future of manufacturing and automation process. Nowadays not many robotic systems are smart systems, in the sense that they operate on a predefined algorithm to do their task. This research focuses on a design and development of a robotic arm with a visual input. The robotic arm will perform its job with the help of visual aid. The system will analyze the input image upon which the decision to write a number using Stochastic Gradient Decent (SGD) algorithm. In a nutshell this research work shows how the neural network can be incorporated with robot arm control, which is a desired field of interest in development of smart robotic systems. This work presents where the robotic arm is incorporated together with a neural network to perform a task of writing numbers using vision

Artificial Neural Networks and Learning Techniques

2016 ◽  
pp. 89-112
Author(s):  
Pushpendu Kar ◽  
Anusua Das
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Work ◽  
The Neural Network ◽  
Learning Techniques ◽  
Learning Procedure ◽  
Processing Component ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

Gestures Identification for Myoelectric Based Control of the Robotic Arm

2020 ◽  
Vol 22 (12) ◽  
Author(s):  
Adhau P ◽  
◽  
Kadwane S. G ◽  
Shital Telrandhe ◽  
Rajguru V. S ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Body ◽  
Human Robot Interaction ◽  
Robotic Arm ◽  
Human Hand ◽  
Robot Interaction ◽  
The Neural Network

Human robot interaction have been ever the topic of research to research scholars owing to its importance to help humanity. Robust human interacting robot where commands from Electromyogram (EMG) signals is recently being investigated. This article involves study of motions a system that allows signals recorded directly from a human body and thereafter can be used for control of a small robotic arm. The various gestures are recognized by placing the electrodes or sensors on the human hand. These gestures are then identified by using neural network. The neural network will thus train the signals. The offline control of the arm is done by controlling the motors of the robotic arm.

Research of object recognition using neural network Inception-v3 model operating on Raspberry Pi B3+

2021 ◽  
Vol 15 (1) ◽  
pp. 13-22
Author(s):  
An Toan Nguyen ◽  
◽  
Ngoc Thien Nguyen ◽  
Thanh Truc Nguyen
Keyword(s):  
Neural Network ◽  
Operating System ◽  
Computer Vision ◽  
Image Classification ◽  
Input Image ◽  
Raspberry Pi ◽  
Practical Applications ◽  
The Neural Network ◽  
Audio Channel ◽  
Compact Size

Image Classification is the most important problem in the field of computer vision. It is very simple and has many practical applications, the image classifier is responsible for assigning a label to the input image from a fixed category group. This article has applied image classification to identify objects by giving the image of the object to be identified, then labeling the image and announcing the label name (object name) through the audio channel. The classification is based on the neural network Inception-v3 model that has been trained on Tensorflow and used Raspberian operating system running on the Raspberry Pi 3 B+ to create a device capable of recognizing objects which compact size and convenient to apply in many fields.

Solution to the Forward and Inverse Kinematic Equations Using Multilayer Back-Propagation Neural Network

1994 ◽  
Author(s):  
Phani K. Nagarjuna ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Kinematic Equation ◽  
Neural Network Approach ◽  
The Neural Network

Abstract The problem of inverse kinematics in Robotics, is a nonlinear mapping from a given cartesian coordinates to the desirable joint coordinates of the robot arm. It is found that an appropriately designed neural network can be trained to learn the non-linearity of the Inverse Kinematic Equation (IKE). We present an approach for solving the Forward Kinematic Equation (FKE) and the IKE by means of a Multi Layer Back-Propagation Neural Network (Rumelhart et al., 1986). The neural network approach is applied to a Two Degrees-of-Freedom (DOF) robot manipulator and the results are compared with those obtained using the analytical solution. The results obtained from the simulation of the neural network indicate a fairly accurate learning of the FKE and IKE by the Multi Layer Back-Propagation Neural Network.

scholarly journals An Optimized Hybrid Neural Network Model for Detecting Depression among Twitter Users

2019 ◽  
Vol 8 (10) ◽  
pp. 2781-2795
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Research Work ◽  
Hybrid Neural Network ◽  
The Neural Network ◽  
Twitter Users ◽  
Trained Neural Network ◽  
User Friendly ◽  
Better Than

The proposed work is to extensively evaluate if a user is depressed or not using his Tweets on Twitter. With the omni presence of social media, this method should help in identifying the depression of users. We propose an Optimized Hybrid Neural Network model to evaluate the user tweets on Twitter to analyze if a user is depressed or not. Where Neural Network is trained using Tweets to predict the polarity of Tweets. The Neural Network is trained in such a way that at any point when presented with a Tweet the model outputs the polarity associated with the Tweet. Also, a user-friendly GUI is presented to the user that loads the trained neural network in no time and can be used to analyze the users’ state of depression. The aim of this research work is to provide an algorithm to evaluate users’ sentiment on Twitter in a way better than all other existing techniques

scholarly journals Product Classification Using Neural Network at Industry Robotic Line

2019 ◽  
Vol 27 (45) ◽  
pp. 55-63
Author(s):  
Igor Halenár ◽  
Gabriela Križanová
Keyword(s):  
Neural Network ◽  
Production Process ◽  
Multilayer Perceptron ◽  
Primary Task ◽  
Robotic Arm ◽  
Product Positioning ◽  
Lighting Conditions ◽  
The Neural Network ◽  
Product Classification ◽  
High Degree

Abstract The article describes a possible way of implementing a neural network in recognizing the shape and position of the products in the production process. The neural network is designed as a multilayer perceptron (MLP), and the whole system is implemented in a form of attachment to robotic arm, where the primary task of neural network is to distinguish a position of product. The neural network is trained like a classifier and outputs are used to control the robot. The advantage of the solution is a high degree of reliability of product positioning under different lighting conditions.

Line Segment-Based Clustering Approach With Self-Organizing Maps

2021 ◽  
Vol 14 (4) ◽  
pp. 33-44
Author(s):  
G. Chamundeswari ◽  
G. P. S. Varma ◽  
C. Satyanarayana
Keyword(s):  
Neural Network ◽  
Line Segment ◽  
Feature Vector ◽  
Input Image ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Clustering Techniques ◽  
Hough Transformation ◽  
The Neural Network ◽  
Self Organizing

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

scholarly journals A Hybrid CKF-NNPID Controller for MIMO Nonlinear Control System

2017 ◽  
Vol 10 (2) ◽  
pp. 176-184
Author(s):  
Adna Sento ◽  
Yuttana Kitjaidure
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Control Method ◽  
Gradient Algorithm ◽  
Experimental Result ◽  
Robotic Arm ◽  
Matlab Simulation ◽  
Rule Model ◽  
The Neural Network ◽  
Cubature Kalman Filter

This paper presents a detailed study to demonstrate the online tuning dynamic neural network PID controller to improve a joint angle position output performance of 4- joint robotic arm. The proposed controller uses a new updating weight rule model of the neural network architecture using multi-loop calculation of the fusion of the gradient algorithm with the cubature Kalman filter (CKF) which can optimize the internal predicted state of the updated weights to improve the proposed controller performances, called a Hybrid CKF-NNPID controller. To evaluate the proposed controller performances, the demonstration by the Matlab simulation program is used to implement the proposed controller that connects to the 4-joint robotic arm system. In the experimental result, it shows that the proposed controller is a superior control method comparing with the other prior controllers even though the system is under the loading criteria, the proposed controller still potentially tracks the error and gives the best performances.

A methodology for fault diagnosis in robotic systems using neural networks

Robotica ◽  
2004 ◽  
Vol 22 (4) ◽  
pp. 419-438 ◽  
Author(s):  
Arun T. Vemuri ◽  
Marios M. Polycarpou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Robotic Manipulators ◽  
Robotic Systems ◽  
Rigid Link ◽  
The Neural Network ◽  
Modeling Uncertainties ◽  
Analytical Redundancy ◽  
Key Issues

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for off-nominal behavior due to faults. The robustness, sensitivity, missed detection and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural network based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.

scholarly journals TOWARDS GLOBAL OPTIMIZATION OF NEURAL NETWORK: A COMPARATIVE ANALYSIS USING GENETIC AND WHALE OPTIMIZATION ALGORITHMS

2021 ◽  
Vol 8 (6) ◽  
pp. 89-101
Author(s):  
E. C. Igodan ◽  
K. C. Ukaoha ◽  
S. O. P. Oliomogbe
Keyword(s):  
Neural Network ◽  
High Speed ◽  
Binary Classification ◽  
Research Work ◽  
Evolutionary Model ◽  
Optimal Number ◽  
Classification Problems ◽  
The Neural Network ◽  
Whale Optimization ◽  
User Friendly

The intelligence and adaptability features of the neural network has made it a technique that is widely used to solve problems in diverse areas such as; detection, monitoring, prediction, diagnostics, data mining, classification, recognition, robotics, biomedicine, etc. However, determination of the optimal number of hidden layers of neural network and other parameters are still a difficult task. Usually, these parameters are decided by trial-and-error which increases the computational complexity and it is human dependent in obtaining the optimal model and parameters alike for any particular task. Optimization has received enormous attention in recent years, primarily because of the rapid progress in computer technology, including the development and availability of user-friendly software, high-speed and parallel processors, and artificial neural networks. This research work is to propose a neuro-evolutionary model using the computational intelligence techniques by combining ANN, GA and WOA for binary classification problems. The proposed optimized ANN-GA and WOA models is to circumvent the problem that is characterized in the trade-off between smoothness and accuracies in selecting the models and optimal parameters of neural network.

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