The Third of the Big 3: Neural Networks

2022 ◽  
pp. 145-173
Author(s):  
Richard V. McCarthy ◽  
Mary M. McCarthy ◽  
Wendy Ceccucci
Keyword(s):  
Neural Networks ◽  
The Third

scholarly journals System for Forecasting COVID-19 Cases Using Time-Series and Neural Networks Models

2021 ◽  
Vol 5 (1) ◽  
pp. 46
Author(s):  
Mostafa Abotaleb ◽  
Tatiana Makarovskikh
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Time Series Models ◽  
Third Wave ◽  
The Third ◽  
Modeling And Forecasting ◽  
Auto Regressive ◽  
The Russian Federation ◽  
R Programming

COVID-19 is one of the biggest challenges that countries face at the present time, as infections and deaths change daily and because this pandemic has a dynamic spread. Our paper considers two tasks. The first one is to develop a system for modeling COVID-19 based on time-series models due to their accuracy in forecasting COVID-19 cases. We developed an “Epidemic. TA” system using R programming for modeling and forecasting COVID-19 cases. This system contains linear (ARIMA and Holt’s model) and non-linear (BATS, TBATS, and SIR) time-series models and neural network auto-regressive models (NNAR), which allows us to obtain the most accurate forecasts of infections, deaths, and vaccination cases. The second task is the implementation of our system to forecast the risk of the third wave of infections in the Russian Federation.

Application Overview of Neural Network in Fault Diagnosis

2014 ◽  
Vol 697 ◽  
pp. 419-424
Author(s):  
Ze Fan Cai ◽  
Dao Ping Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Development Trend ◽  
System Structure ◽  
Bp Network ◽  
Rbf Network ◽  
The Third ◽  
The Neural Network ◽  
The Development Trend

This paper introduces the system structure of neural network in fault diagnosis, and summarizes some applications of neural network in fault diagnosis. The most commonly used neural network in fault diagnosis is BP network. The second is RBF network and the third is ART. For each neural network, the paper will discuss the neural network, and the introduce some applications. It also introduces the combination of neural networks and other techniques. In the last part, this paper points out the development trend of the neural network in fault diagnosis.

Adaptive control of a robot using neural networks

Robotica ◽  
1994 ◽  
Vol 12 (6) ◽  
pp. 553-561 ◽  
Author(s):  
D. T. Pham ◽  
S. J. Oh
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Control System ◽  
Inverse Dynamics ◽  
Dynamic Interaction ◽  
Variable Load ◽  
Time Varying ◽  
Adaptive Control System ◽  
The Third ◽  
Articulated Robot

SummaryThis paper describes an adaptive control system for an articulated robot with n joints carrying a variable load. The robot is a complex nonlinear time-varying MIMO plant with dynamic interaction between its inputs and outputs. However, the design of the control system is relatively straightforward and does not require any prior knowledge about the plant. This is because the control system is based on using neural networks which can capture the dynamic characteristics of the plant automatically. Three neural networks are employed in total, the first to learn the dynamics of the robot, the second to model its inverse dynamics and the third, a copy of the second neural network, to control the robot.

The third anniversary of Neural Networks

1991 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Stephen Grossberg
Keyword(s):  
Neural Networks ◽  
The Third

Automated Whale Blow Detection in Infrared Video

2015 ◽  
pp. 58-78
Author(s):  
Varun Santhaseelan ◽  
Vijayan K. Asari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Infrared Camera ◽  
Future Research ◽  
Multi Layer Perceptron ◽  
Three Solutions ◽  
The Third ◽  
Manual Intervention ◽  
Infrared Video ◽  
Spatio Temporal

In this chapter, solutions to the problem of whale blow detection in infrared video are presented. The solutions are considered to be assistive technology that could help whale researchers to sift through hours or days of video without manual intervention. Video is captured from an elevated position along the shoreline using an infrared camera. The presence of whales is inferred from the presence of blows detected in the video. In this chapter, three solutions are proposed for this problem. The first algorithm makes use of a neural network (multi-layer perceptron) for classification, the second uses fractal features and the third solution is using convolutional neural networks. The central idea of all the algorithms is to attempt and model the spatio-temporal characteristics of a whale blow accurately using appropriate mathematical models. We provide a detailed description and analysis of the proposed solutions, the challenges and some possible directions for future research.

scholarly journals Wind Speed Forecasting by Wavelet Neural Networks: A Comparative Study

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Chuanan Yao ◽  
Xiankun Gao ◽  
Yongchang Yu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wind Speed ◽  
Vital Role ◽  
Wavelet Neural Network ◽  
System Stability ◽  
Wavelet Neural Networks ◽  
Grid Security ◽  
The Third ◽  
Combined Models

Due to the environmental degradation and depletion of conventional energy, much attention has been devoted to wind energy in many countries. The intermittent nature of wind power has had a great impact on power grid security. Accurate forecasting of wind speed plays a vital role in power system stability. This paper presents a comparison of three wavelet neural networks for short-term forecasting of wind speed. The first two combined models are two types of basic combinations of wavelet transform and neural network, namely, compact wavelet neural network (CWNN) and loose wavelet neural network (LWNN) in this study, and the third model is a new hybrid method based on the CWNN and LWNN models. The efficiency of the combined models has been evaluated by using actual wind speed from two test stations in North China. The results show that the forecasting performances of the CWNN and LWNN models are unstable and are affected by the test stations selected; the third model is far more accurate than the other forecasting models in spite of the drawback of lower computational efficiency.

SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS

2004 ◽  
Vol 18 (04) ◽  
pp. 563-581 ◽  
Author(s):  
BORIS CIGALE ◽  
DAMJAN ZAZULA
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Recognition Rate ◽  
Cellular Neural Networks ◽  
Second Step ◽  
Ultrasound Images ◽  
Segmentation Method ◽  
The Third ◽  
Fourth Step

Segmentation of ovarian ultrasound images using cellular neural networks (CNNs) is studied in this paper. The segmentation method consists of five successive steps where the first four uses CNNs. In the first step, only rough position of follicles is determined. In the second step, the results are improved by expansion of detected follicles. In the third step, previously undetected inexpressive follicles are determined, while the fourth step detects the position of ovary. All results are joined in the fifth step. The templates for CNNs were obtained by applying genetic algorithm. The segmentation method has been tested on 50 ovarian ultrasound images. The recognition rate of follicles was around 60% and misidentification rate was around 30%.

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