Artificial Neural Networks Technology

Preface We have made this report file on the topic neural network; we have tried our best to elucidate (clarify) all the relevant detail to the topic to be included in the report. While in the beginning we have tried to give a general view about this topic. Our efforts and wholehearted co-corporation of each and everyone has ended on a successful note. we express our sincere gratitude to MR UGWUNNA CHARLES O. who have been there for us throughout the preparation of this topic. We thank

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.

—An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. This paper gives overview of Artificial Neural Network, working & training of ANN. It also explain the application and advantages of ANN.

Computers & Mathematics with Applications, 1996

The presented technical report is a preliminary English translation of selected revised sections from the rst part of the book Theoretical Issues of Neural Networks 75] by the rst author which represents a brief introduction to neural networks. This work does not cover a complete survey of the neural network models but the exposition here is focused more on the original motivations and on the clear technical description of several basic type models. It can be understood as an invitation to a deeper study of this eld. Thus, the respective b a c kground is prepared for those who have not met this phenomenon yet so that they could appreciate the subsequent theoretical parts of the book. In addition, this can also be pro table for those engineers who want t o a p p l y the neural networks in the area of their expertise. The introductory part does not require deeper preliminary knowledge, it contains many pictures and the mathematical formalism is reduced to the lowest degree in the rst chapter and it is used only for a technical description of neural network models in the following chapters. We will come back to the formalization of some of these introduced models within their theoretical analysis. The rst chapter makes an e ort to describe and clarify the neural network phenomenon. It contains a brief survey of the history of neurocomputing and it explains the neurophysiological motivations which led to the mathematical model of a neuron and neural network. It shows that a particular model of the neural network can be determined by means of the architectural, computational, and adaptive dynamics that describe the evolution of the speci c neural network parameters in time. Furthermore, it introduces neurocomputers as an alternative to the classical von Neumann computer architecture and the appropriate areas of their applications are discussed. The second chapter deals with the classical models of neural networks. First, the historically oldest model | the network of perceptrons is shortly mentioned. Further, the most widely applied model in practice | the multi{layered neural network with the back-propagation learning algorithm, is described in detail. The respective description, besides various variants of this model, contains implementation comments as well. The explanation of the linear model MADALINE, adapted according to the Widrow rule, follows. The third chapter is concentrated on the neural network models that are exploited as autoassociative or heteroassociative memories. The principles of the adaptation according to Hebb law are explained on the example of the linear associator neural network. The next model is the well{known Hop eld network, motivated by p h ysical theories, which is a representative of the cyclic neural networks. The analog version of this network can be used for heuristic solving of the optimization tasks (e. g. traveling salesman problem). By the physical analogy, a temperature parameter is introduced into the Hop eld network and thu s , a s t o c hastic model, the so{called Boltzmann machine is obtained. The information from this part of the book can be found in an arbitrary monograph or in survey articles concerning neural networks. For its composition we issued namely from the works 16, 24, 26, 27, 35, 36, 45, 73].

Digital Systems, 2018

Due to the recent trend of intelligent systems and their ability to adapt with varying conditions, deep learning becomes very attractive for many researchers. In general, neural network is used to implement different stages of processing systems based on learning algorithms by controlling their weights and biases. This chapter introduces the neural network concepts, with a description of major elements consisting of the network. It also describes different types of learning algorithms and activation functions with the examples. These concepts are detailed in standard applications. The chapter will be useful for undergraduate students and even for postgraduate students who have simple background on neural networks.

2020

A neural network is a chain of logics and algorithms which caters to the recognition of dependent entities and relationships in a set of data, in a manner works very much in the same manner as the regular homo sapien brain does. Henceforth, neural networks can extend their references to a configuration of neurons, which may have a synthetic setup as well, in addition to the natural one. Also, these neural networks are self-responsive to the variable inputs and generates the best outputs without any requirements of re-designing. The flowline covers up the content as follows: firstly we discuss the insights of what a neural network is in pure layman terms, henceforth the definition of CNN and NN have been showcased, after that the research literature sheds light on the actual model of our research and its representative technique has been showcased, with a code snippet excerpt which stands self-explanatory. The net bestows and the results of the proposed model have also been pasted al...

Automatic Learning Techniques in Power Systems, 1998

Journal of the Society of Dyers and Colourists, 1998

European Journal of Gastroenterology & Hepatology, 2007

This paper is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Finally, the mathematical models involved are presented and demonstrated. During the last ten years neural networks have shown their worth. The success of a neural network approach is deeply dependent on the right network architecture. The architecture of a neural network determines the number of neurons in the network and the topology of the connections within the network. The emphasis of this paper is on automatic generation of network architecture.

2014

Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. Neural networks, have remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. so in this paper we tried to introduce a brief overview of ANN to help researchers in their way throw ANN.

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.