Fundamentals of Neural Networks

The purpose of this essay is to provide the reader with a general definition of Artificial Neural Networks; their functioning, applications, and a few words about what the future might allow in this area.

As a machine learning algorithm, neural network has been widely used in various research projects to solve various critical problems. The concept of neural networks is inspired from the human brain. The paper will explain the actual concept of Neural Networks such that a non-skilled person can understand basic concept and also make use of this algorithm to solve various tedious and complex problems. The paper demonstrates the designing and implementation of fully design Neural Network along with the codes. It gives various architectures of ANN also the advantages, disadvantages & applications.

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.

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.

Automatic Learning Techniques in Power Systems, 1998

ABSTRACT: The study introduces a variety of fuzzy set-oriented neurons, proposes architectures of neural networks and addresses the fundamental issues oftheir leaming. Subsequently the applications of these networks are studied in detail. A particular emphasis is focused on an explicit character of knowledge representation realized by these networks significantly facilitating their leaming and interpretation.

IJRASET, 2021

In this paper an overview on neural network and its application is focused. In Real-world business applications for neural networks are booming. In some cases, NNs have already become the method of choice for businesses that use hedge fund analytics, marketing segmentation, and fraud detection. Here are some neural network innovators who are changing the business landscape. Here shown that how the biological model of neural network functions, all mammalian brains consist of interconnected neurons that transmit electrochemical signals. Neurons have several components: the body, which includes a nucleus and dendrites; axons, which connect to other cells; and axon terminals or synapses, which transmit information or stimuli from one neuron to another. Combined, this unit carries out communication and integration functions in the nervous system.

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.

Deep neural networks (DNNs) are rapidly being used in safety-critical areas such as drone and aircraft control, supporting techniques for analyzing the safety of actions. Unfortunately, DNN analysis is NP-hard and existing algorithms become slower as the number of nodes in the DNN increases. Neural networks, a subset of artificial intelligence, have rapidly evolved, transforming the landscape of machine learning. Inspired by the structure and function of the human brain, these computational models have demonstrated exceptional capabilities in various applications. This research paper provides a comprehensive analysis of neural networks, encompassing their historical development, architectural components, training methodologies, real-world applications, existing challenges, and future directions.

Computer, 1996

Numerous e orts have been made in developing \intelligent" programs based on the Von Neumann's centralized architecture. However, these e orts have not been very successful in building general-purpose intelligent systems. Inspired by biological neural networks, researchers in a number of scienti c disciplines are designing arti cial neural networks (ANNs) to solve a variety of problems in decision making, optimization, prediction, and control. Arti cial neural networks can be viewed as parallel and distributed processing systems which consist of a huge number of simple and massively connected processors. There has been a resurgence of interest in the eld of ANNs for several years. This article intends to serve as a tutorial for those readers with little or no knowledge about ANNs to enable them to understand the remaining articles of this special issue. We discuss the motivations behind developing ANNs, basic network models, and two main issues in designing ANNs: network architecture and learning process. We also present one of the most successful application of ANNs, namely automatic character recognition.

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 is the combination of science and technology for making and using its best in future. it is usually means using AI to understand human intelligence in the form of machine by the means of conceptual definition of Artificial intelligence, it is broadly characterized as the study of computations and machines which usually deals with reasoning, questioning, perception and its action. So basically this paper examines the brief introduction on ANN and history of ANN under AI , its existing application and its ideas with the implementation.

2020

In this paper, an overview of the artificial neural networks is presented. Their main and popular types such as the multilayer feedforward neural network (MLFFNN), the recurrent neural network (RNN), and the radial basis function (RBF) are investigated. Furthermore, the main advantages and disadvantages of each type are included as well as the training process.