Review of Neural Network Algorithms

The artificial neural network is the core tool of machine learning to realize intelligence. It has shown its advantages in the fields of sound, image, sound, picture, and so on. Since entering the 21st century, the progress of science and technology and people\u27s pursuit of artificial intelligence have introduced the research of artificial neural networks into an upsurge. Firstly, this paper introduces the application background and development process of the artificial neural network in order to clarify the research context of neural networks. Five branches and related applications of single-layer perceptron, linear neural network, BP neural network, Hopfield neural network, and depth neural network are analyzed in detail. The analysis shows that the development trend of the artificial neural network is developing towards a more general, flexible, and intelligent direction. Finally, the future development of the artificial neural network in training mode, learning mode, function expansion, and technology combination has prospected

The High Time Resolution Universe Survey VI: An Artificial Neural Network and Timing of 75 Pulsars

We present 75 pulsars discovered in the mid-latitude portion of the High Time Resolution Universe survey, 54 of which have full timing solutions. All the pulsars have spin periods greater than 100 ms, and none of those with timing solutions are in binaries. Two display particularly interesting behaviour; PSR J1054-5944 is found to be an intermittent pulsar, and PSR J1809-0119 has glitched twice since its discovery. In the second half of the paper we discuss the development and application of an artificial neural network in the data-processing pipeline for the survey. We discuss the tests that were used to generate scores and find that our neural network was able to reject over 99% of the candidates produced in the data processing, and able to blindly detect 85% of pulsars. We suggest that improvements to the accuracy should be possible if further care is taken when training an artificial neural network; for example ensuring that a representative sample of the pulsar population is used during the training process, or the use of different artificial neural networks for the detection of different types of pulsars.Comment: 15 pages, 8 figure

Artificial Neural Network

The long course of evolution has given the human brain many desirable characteristics not present in Von Neumann or modern parallel computers. These include massive parallelism, distributed representation and computation, learning ability, generalization ability,adaptivity, inherent contextual information processing, fault tolerance, and low energy consumption. It is hoped that devices based on biological neural networks will possess some of these desirable characteristics.On this basic we come out with the concept of artificial neural network. An artificial neural network, often just called a neural network, is a mathematical model inspired by biological neural networks. A neural network consists of an interconnected group of artificial neurons, and it processes information using a connectionist approach to computation. Neural networks have emerged in the past few years as an area of unusual opportunity for research, development and application to a variety of real world problems. Indeed, neural networks exhibit characteristics and capabilities not provided by any other technology. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron. This paper presents a brief tutorial on artificial neural networks, some of the most commonly used ANN models and briefly describes several applications of it

Human activity recognition making use of long short-term memory techniques

The optimisation and validation of a classifiers performance when applied to real world problems is not always effectively shown. In much of the literature describing the application of artificial neural network architectures to Human Activity Recognition (HAR) problems, postural transitions are grouped together and treated as a singular class. This paper proposes, investigates and validates the development of an optimised artificial neural network based on Long-Short Term Memory techniques (LSTM), with repeated cross validation used to validate the performance of the classifier. The results of the optimised LSTM classifier are comparable or better to that of previous research making use of the same dataset, achieving 95% accuracy under repeated 10-fold cross validation using grouped postural transitions. The work in this paper also achieves 94% accuracy under repeated 10-fold cross validation whilst treating each common postural transition as a separate class (and thus providing more context to each activity)

Implementation of a Neuromorphic Development Platform with DANNA

Neuromorphic computing is the use of artificial neural networks to solve complex problems. The specialized computing field has been growing in interest during the past few years. Specialized hardware that function as neural networks can be utilized to solve specific problems unsuited for traditional computing architectures such as pattern classification and image recognition. However, these hardware platforms have neural network structures that are static, being limited to only perform a specific application, and cannot be used for other tasks. In this paper, the feasibility of a development platform utilizing a dynamic artificial neural network for researchers is discussed

Development of a dc-ac power conditioner for wind generator by using neural network

This project present of development single phase DC-AC converter for wind generator application. The mathematical model of the wind generator and Artificial Neural Network control for DC-AC converter is derived. The controller is designed to stabilize the output voltage of DC-AC converter. To verify the effectiveness of the proposal controller, both simulation and experimental are developed. The simulation and experimental result show that the amplitude of output voltage of the DC-AC converter can be controlled

Utilizing Soft Computing for Determining Protein Deficiency

Abstract— In recent years, the occurrence of protein shortage of children under 5 years old in many poor area has dramatically increased. Since this situation can cause serious problem to children like a delay in their growth, delay in their development and also disfigurement, disability, dependency, the early diagnose of protein shortage is vital. Many applications have been developed in performing disease detection such as an expert system for diagnosing diabetics and artificial neural network (ANN) applications for diagnosing breast cancer, acidosis diseases, and lung cancer. This paper is mainly focusing on the development of protein shortage disease diagnosing application using Backpropagation Neural Network (BPNN) technique. It covers two classes of protein shortage that are Heavy Protein Deficiency. On top of this, a BPNN model is constructed based on result analysis of the training and testing from the developed application. The model has been successfully tested using new data set. It shows that the BPNN is able to early diagnose heavy protein deficiency accurately. Keywords— Artificial Neural Network, Backpropagation Neural Network, Protein Deficiency

PREDIKSI HARGA SAHAM MENGGUNAKAN ALGORITMA NEURAL NETWORK

In recent years, the development of technology and artificial intelligence has brought forth new opportunities in analyzing and predicting stock prices. One of the approaches used is the Neural Network algorithm, which is a part of the branch of artificial intelligence known as Deep Learning. This algorithm can learn complex patterns and relationships among data by modeling inspired by the human neural network. This research utilizes the Neural Network for stock price prediction and aims to understand the application of Neural Network in predicting stock prices, which can benefit investors and market participants. Additionally, historical stock price data can be used as input for the Neural Network algorithm. The Neural Network is a frequently used algorithm for accurate predictions and is widely employed in prediction-based or forecasting research. The result of this research is the Root Mean Squared Error (RMSE) value of 19.734 +/- 0.000. The use of the Neural Network as an algorithm for stock price prediction provides investors with valuable information for making investment decisions for companies.

OPTIMIZATION TECHNIQUE FOR SOFTWARE COST ESTIMATION USING NEURAL NETWORK

Last few decade software accomplishment admiration models developed, authentic estimates of the software activity beneath development is still unachievable goal. Recently advisers are alive on the development of new models and the advance of the absolute ones application bogus intelligence techniques. Designing of ANN (Artificial Neural Network) to archetypal a circuitous set of accord amid the abased capricious (effort) and the absolute variables (cost drivers) makes an apparatus for estimation. This cardboard presents an achievement assay of Multi ANNs in accomplishment estimation. We accept apish Back propagation ANN created by MATLAB Neural Network Apparatus application NASA dataset

An Evaluation Of Artificial Neural Network In Predicting The Presence Of Heart Disease

The purpose of this study is to evaluate the application of artificial neural network in predicting the presence of heart disease, particularly the angina in patients that already diagnosed with myocardial infarction. The prediction and detection of angina is important in determining the most appropriate form of treatment for these patients. Furthermore, diagnosis and management of angina is important since it can lead to the recurrent of myocardial infarction. The development of the application involves three main phases. The first phase is the development of Myocardial Infarction Management Information System (MIMIS) for data collection and management. Then followed by the second phase, which is the development of Neural Network Simulator (NNS) using back propagation for network training and testing. The final phase is the development of Prediction System (PS) for prediction on new patient’s data. All systems had been developed using Microsoft’s Visual Basics. The data used to train and test the network was provided by Alor Setar General Hospital, Kedah. The best network model produced prediction accuracy of 88.89 percents. Apart from proving the ability of neural network technology in medical diagnosis, this study also shown how the neural network could be integrated into a management information system as a prediction tools. As the pilot project, the application developed could be used as the starting point in building a medical decision support system, particularly in diagnosing the heart disease