Artificial neural networks: a tutorial (original) (raw)

Artificial Neural Networks and their Applications

Corr, 2005

The Artificial Neural Network (ANN) is a functional imitation of simplified model of the biological neurons and their goal is to construct useful 'computers' for real-world problems and reproduce intelligent data evaluation techniques like pattern recognition, classification and generalization by using simple, distributed and robust processing units called artificial neurons. ANNs are fine-grained parallel implementation of non-linear static-dynamic systems. The intelligence of ANN and its capability to solve hard problems emerges from the high degree of connectivity that gives neurons its high computational power through its massive parallel-distributed structure. The current resurgent of interest in ANN is largely because ANN algorithms and architectures can be implemented in VLSI technology for real time applications. The number of ANN applications has increased dramatically in the last few years, fired by both theoretical and application successes in a variety of disciplines. This paper presents a survey of the research and explosive developments of many ANN-related applications. A brief overview of the ANN theory, models and applications is presented. Potential areas of applications are identified and future trend is discussed.

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.

Ani1 K WHY ARTIFICIAL NEURAL NETWORKS?

umerous advances have been made in developing intelligent systems, some inspired by biological neural networks. N Researchers from many scientific disciplines are designing artificial neural networks (A"s) to solve a variety of problems in pattern recognition, prediction, optimization, associative memory, and control (see the "Challenging problems" sidebar).

Neural networks approach v/s Algorithmic approach : A study through pattern recognition

Advanced Computing: An International Journal, 2011

There is a great scope of expansion in the field of Neural Network, as it can be viewed as massively parallel computing systems consisting of an extremely large number of simple processors with many interconnections. NN models attempt to use some organizational principles in a weighted directed graphs in which nodes are artificial neurons and directed edges are connections between neuron outputs and neuron inputs. The main characteristic of neural network is that they have the ability to learn complex non-linear input output relationships. A single artificial neuron is a simulation of a neuron (basic human brain cell) and scientists have tried to emulate the neuron in a form of artificial neuron called perceptron. Pattern recognition is one of the areas where the neural approach has been successfully tried. This study is concerned to see the journey of pattern recognition from algorithmic approach to neural network approach.

Artificial Neural Network: A brief study

IRJET, 2023

An Artificial Neural Network (ANN) is a data processing paradigm inspired by the way biological nervous systems, such as the brain, process data. The unique structure of the information processing system is a crucial component of this paradigm. It is made up of a huge number of highly interconnected processing elements (neurons) that work together to solve issues. ANNs, like humans, learn by example, and a huge dataset results in more accuracy. Through a learning process, an ANN is trained for a specific application, such as pattern recognition or data classification. This is also true of ANNs. This paper provides an overview of Artificial Neural Networks (ANN), their working, and training. It also describes the application and benefits of ANN.

ARTIFICIAL NEURAL NETWORKS – ARCHITECTURES AND APPLICATIONS

Artificial neural networks may probably be the single most successful technology in the last two decades which has been widely used in a large variety of applications. The purpose of this book is to provide recent advances of architectures, methodologies, and applications of artificial neural networks. The book consists of two parts: the architecture part covers architectures, design, optimization, and analysis of artificial neural networks; the applications part covers applications of artificial neural networks in a wide range of areas including biomedical, industrial, physics, and financial applications. Thus, this book will be a fundamental source of recent advances and applications of artificial neural networks. The target audience of this book includes college and graduate students, and engineers in companies.

Artificial Neural Network Systems

2021

Artificial Neural Networks is a calculation method that builds several processing units based on interconnected connections. The network consists of an arbitrary number of cells or nodes or units or neurons that connect the input set to the output. It is a part of a computer system that mimics how the human brain analyzes and processes data. Self-driving vehicles, character recognition, image compression, stock market prediction, risk analysis systems, drone control, welding quality analysis, computer quality analysis, emergency room testing, oil and gas exploration and a variety of other applications all use artificial neural networks. Predicting consumer behavior, creating and understanding more sophisticated buyer segments, marketing automation, content creation and sales forecasting are some applications of the ANN systems in the marketing. In this paper, a review in recent development and applications of the Artificial Neural Networks is presented in order to move forward the research filed by reviewing and analyzing recent achievements in the published papers. Thus, the developed ANN systems can be presented and new methodologies and applications of the ANN systems can be introduced.