Pattern Recognition in Neural Networks (original) (raw)
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Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain (called, Artificial Neural Network) that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because, the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research, now a day’s pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural network(in the algorithm of artificial Intelligence) as the best possible way of utilizing available resources to make a decision that can be a human like performance.
Backpropagation Algorithm: An Artificial Neural Network Approach for Pattern Recognition
— The concept of pattern recognition refers to classification of data patterns and distinguishing them into predefined set of classes. There are various methods for recognizing patterns studied under this paper. The objective of this review paper is t o summarize the methods used in various stages of a pattern recognition system and identify the best suitable technique with its advantages over other techniques to recognize the complex patterns along with other real-life applications.
Neural Networks and Its Learning Techniques
International Conference on Information Engineering, Management and Security 2014, 2014
A neural network is an artificial representation of the human brain that tries to simulate its learning process. An artificial neural network (ANN) is often called a "Neural Network " or simply Neural Net (NN). This paper summarizes the some of the most important developments in neural network and its learning techniques. Learning can be done in supervised or unsupervised training. Our purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics.
Use of Artificial Neural Network in Pattern Recognition
Among the various traditional approaches of pattern recognition the statistical approach has been most intensively studied and used in practice. More recently, the addition of artificial neural network techniques theory have been receiving significant attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.
An Analysis of the Perceptron-Multilayer Algorithm and its Applications
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This paper provides a comprehensive examination of the perceptron and multilayer perceptron (MLP) models, em- phasizing their historical significance and practical applications in artificial intelligence and machine learning. It begins with an overview of the perceptron, introduced by Frank Rosenblatt in 1958, as a foundational element in neural network research, capable of solving linear classification problems. The paper discusses the limitations of single-layer perceptrons, particularly their inability to address non-linear problems like the XOR problem, which led to the development of multilayer perceptrons and the backpropagation algorithm in the 1980s. The study details the implementation of a simple neural network with one hidden layer using C++, focusing on key com- ponents such as activation functions, weight updates, and training methods. It also explores the integration of the perceptron model with hardware components, specifically using the ESP32 microcontroller to demonstrate real-world applications, including controlling LEDs based on model predictions. Furthermore, the paper evaluates the performance and generalization capabilities of both perceptron and multilayer perceptron models through training and validation datasets. In addition to practical implementations, the paper discusses the evolution of neural network architectures, including convo- lutional and recurrent neural networks, and their relevance in solving complex problems beyond the scope of simpler models. The findings underscore the importance of understanding the perceptron as a stepping stone in the broader context of neural network research and its implications for future advancements in artificial intelligence.
Analysis of Pattern Recognition by Neural Network
2012
Pattern recognition basically assigns a label to a given input image. Pattern recognition is done on the basis of classes to which an input image belongs. A pattern could be a fingerprint image, a handwritten cursive word, a human face, or a speech signal. In this paper we consider to analyze back propagation algorithm and feed forward algorithm used for recognizing patterns. We also try to implement Leaky integrate and fire neuron model which belongs to a category of Spiking neural networks. KeywordsBack propogation Algorithm, Feed Forward Algorithm, LIF-model, Spiking Neural Network.
A REVIEW ON PATTERN RECOGNITION MODELS
IAEME PUBLICATION, 2020
Pattern Recognition is widely used in many fields of computer science and medical science. It is one of artificial intelligence's actively searched and very important branches. It can be called a science that attempts to develop machines as smart as humans for recognizing patterns and then categorizing in a reliable and simple way into desired categories. In the last few decades, researches attention has been grabbed by pattern recognition as an approach of learning because it has wide areas of application. Its applications include drug, information systems, automation, data mining, military intelligence, document classification, bioinformatics, business, speech recognition, and several others. Various approaches to Pattern Recognition have been presented in this review paper and their pros-cons have shown the application of a specific paradigm. Depending on the surevy, the techniques of pattern recognition are categorized in six parts. They are Structural Techniques, Statistical Techniques, Neural Network Approach, Template Matching, Hybrid Models and Fuzzy Model.