Artificial Neural Networks in Engineering Education (original) (raw)

Basic Application and Study of Artificial Neural Networks

In this paper, we are expounding Artificial Neural Network or ANN, its different qualities and business applications. In this paper we additionally demonstrate that "what are neural systems" and "Why they are so essential in today's Artificial knowledge?" Because various advances have been made in creating Intelligent framework, some roused by natural neural systems. ANN gives an exceptionally energizing choices and other application which can assume imperative part in today's software, Computer engineering field. There are a few Limitations likewise which are said. An Artificial Neural Network (ANN) is a data handling worldview that is motivated by the way natural sensory systems, for example, the mind, prepare data. The key component of this worldview is the novel structure of the data preparing framework. It is made out of an extensive number of exceptionally interconnected handling components (neurons) working as one to take care of particular issues. ANNs, similar to individuals, learn by illustration. An ANN is designed for a particular application, for example, design acknowledgment or information arrangement, through a learning procedure. Learning in natural frameworks includes conformity to the synaptic associations that exist between the neurons. This is valid for ANNs too. This paper gives outline of Artificial Neural Network, working and preparing of ANN. It additionally clarifies the application and points of interest of ANN.

ARTIFICIAL NEURAL NETWORK BASED INTELLIGENT SYSTEM

In information technology, a neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships .A program can then tell the network how to behave in response to an external stimulus .For example, to input from a computer user who is interacting with the network or can initiate activity on its own within the limits of its access to the external world. The main features of this paper involves The basic idea of what is " A neural network ". The tools used in it. The three main applications of this phenomenon in the real time world. The first application includes the using of neural networks for the visual perception. The project of Receptive-Field Laterally Interconnected Synergetically Self-Organizing Map (RF-LISSOM) model of the primary visual cortex is explained. Secondly, the usage of neural networks to Control of robotic arms which works on their own in the industries. Finally, the Speech Recognition Using Neural Networks for Spoken Language Understanding. Everything from handwriting and speech recognition to stock market prediction will become more sophisticated as researchers develop better training methods and network architectures. The continuing advances in computer technology allow for the invention of ever more complex networks, eventually allowing us to exceed even the complexity of the human mind.

A STUDY ON ARTIFICIAL NEURAL NETWORKS

A STUDY, 2018

First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.

Understanding Artificial Neural Networks and its formation

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2024

A brain network is an information handling framework comprising of countless basic, exceptionally interconnected handling components in an engineering motivated by the construction of the cerebral cortex piece of the cerebrum. Thus, brain networks are frequently equipped for doing things which people or creatures get along nicely however which customary PCs frequently do inadequately. Brain networks have arisen in the beyond couple of years as an area of surprising an open door for research, improvement and application to various true issues. For sure, brain networks display attributes and abilities not given by some other innovation.

Teaching neural networks concepts and their learning techniques

Neural networks have become increasing popular in the fields of Science and Engineering over the last decade. Most graduate schools in the United States of America and probably in other parts of the world have started offering neural networks as a graduate/postgraduate course. Neural networks are used for nonlinear systems modeling, estimation and prediction of parameters, pattern matching, identification and control. It is necessary that engineering students learn the basics of neural networks somewhere in their undergraduate degree program without taking a full quarter/semester course. This paper presents a software developed in Java to teach basic neural network concepts with backpropagation based learning in a couple of weeks for undergraduate engineering students. This can be done as part of a modeling and simulation course in various disciplines of Engineering.

An educational tool for artificial neural networks

Computers & Electrical Engineering, 2011

Artificial neural networks are some kind of data processing systems, which try to simulate features of the human brain and its learning process. So, they are widely used by researchers to solve different problems in optimization, classification, pattern recognition, associative memory and control. In this paper, an educational tool, which can be used to work on different kinds of neural network models and learn fundamentals of the artificial neural network, is described. At this point, the whole tool environment provides an advanced system to ensure mentioned functions. The developed system supports using MLP, LVQ and SOM models and related learning algorithms. It employs some visual, interactive tools, which enable users to compose their own neural networks and work on the developed networks easily. By using these tools, users can also understand and learn working mechanism of a typical artificial neural network, using features of different models and related learning algorithms.

Artificial Intelligence & Neural Networks II

One of the greatest mysteries of science is in the elusiveness of knowing exactly how the brain and thus the mind makes thought possible. The phenomenon of unlocking the secrets of the brain and therefore understanding its fundamental areas of function represents one of the greatest challenges of our time. According to most neural network researchers, the objective of AI is to seek an understanding of nature's capabilities for which the human race can engineer solutions to problems that cannot be solved by traditional computing. This paper looks at the biological human brain as the inspiration behind AI.

Advanced Neural Network Applied In Engineering Science

The basic idea behind a neural network is to simulate (copy in a simplified but reasonably faithful way) lots of densely interconnected brain cells inside a computer so you can get it to learn things, recognize patterns, and make decisions in a humanlike way. The amazing thing about a neural network is that you don't have to program it to learn explicitly: it learns all by itself, just like a brain! But it isn't a brain. It's important to note that neural networks are (generally) software simulations: they're made by programming very ordinary computers, working in a very traditional fashion with their ordinary transistors and serially connected logic gates, to behave as though they're built from billions of highly interconnected brain cells working in parallel. This paper is to propose that a neural network applied in engineering science that how a robots that can see, feel, and predict the world around them, improved stock prediction, common usage of self-driving car and much more!

Neural networks and its application in engineering

… Science & IT …, 2009

Neural Network (NN) has emerged over the years and has made remarkable contribution to the advancement of various fields of endeavor. The purpose of this work is to examine neural networks and their emerging applications in the field of engineering, focusing more on Controls. In this work, we have examined the various architectures of NN and the learning process. The needs for neural networks, training of neural networks, and important algorithms used in realizing neural networks have also been briefly discussed. Neural network application in control engineering has been extensively discussed, whereas its applications in electrical, civil and agricultural engineering were also examined. We concluded by identifying limitations, recent advances and promising future research directions.

Artificial Intelligence & Neural Networks

One of the greatest mysteries of science is in the elusiveness of knowing exactly how the brain and thus the mind makes thought possible. The phenomenon of unlocking the secrets of the brain and therefore understanding its fundamental areas of function represents one of the greatest challenges of our time. According to most neural network researchers, the objective of AI is to seek an understanding of nature's capabilities for which the human race can engineer solutions to problems that cannot be solved by traditional computing. This paper takes a basic looks at the physical biological brain as the motivation behind AI.