Control Systems in Manufacturing

1994 ◽  
Author(s):  
Mruthyunjaya S. Telagi ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Systems ◽  
Research Trends ◽  
Future Research ◽  
Cerebellar Model Articulation Controller ◽  
Manufacturing Environment ◽  
The Future ◽  
Planar Robot

Abstract This paper reviews different control methodologies applied in manufacturing environment. Since comparatively newer control methodologies like — Neural networks, Fuzzy logic, and Cerebellar model articulation controller have gained more research interests in recent years, they have been dealt in more detail. With this, we have presented application of neural network for endeffector positioning of three degree planar robot and results have been evaluated. Finally the future research trends in these areas have been discussed.

scholarly journals The research on COVID-19 and economy from 2019 to 2020: analysis from the perspective of bibliometrics

2021 ◽  
Vol 12 (2) ◽  
pp. 217-268
Author(s):  
Nana Liu ◽  
Zeshui Xu ◽  
Marinko Skare
Keyword(s):  
Statistical Analysis ◽  
Value Added ◽  
Research Trends ◽  
Research Progress ◽  
Future Research ◽  
Human Society ◽  
Citation Networks ◽  
The Future ◽  
Meaningful Research

Research background: The outbreak and spread of COVID-19 brought disastrous influences to the development of human society, especially the development of economy. Purpose of the article: Considering that knowing about the situations of the existing studies about COVID-19 and economy is not only helpful to understand the research progress and the connections between COVID-19 and economy, but also provides effective suggestions for fighting against COVID-19 and protecting economy, this paper analyzes the existing studies on COVID-19 and economy from the perspective of bibliometrics. Methods: Firstly, the discussion starts from the statistical analysis, in which the basic distributions of the studies on different countries/regions, different publication sources, different publication years, etc., are presented. Then, the paper shows the cooperation situations of the researchers from analyzing the related citation networks, co-citation networks and cooperation networks. Further, the theme analysis of the related studies is presented, in which the related co-occurrence networks are shown, and then the detailed analyses of the studies are introduced. Based on these analyses, the discussions about future research are presented, and finally we draw a conclusion. Findings & value added: The analyses not only present the basic situation on the research about COVID-19 and Economy, but also show the future research trends, which can provide meaningful research expectations.

Applications of Artificial Neural Networks in Economics and Finance

2018 ◽  
pp. 6631-6641
Author(s):  
Iva Mihaylova
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Research Trends ◽  
Dependence Analysis ◽  
Future Trends ◽  
Powerful Technique ◽  
Analytic Tool ◽  
The Future ◽  
Multivariate Dependence ◽  
Artificial Neural

Artificial neural Networks (ANNs) are a powerful technique for multivariate dependence analysis. Originally inspired by neuroscience, ANNs are becoming an increasingly attractive analytic tool for applications in the area of economics and finance due to the flexible solutions they offer. The purpose of this article is to present such important applications with an emphasis on recent research trends. The contributions are grouped as follows: ANNs (1) for prediction, (2) for classification and (3) for modelling. The chapter concludes with the future trends in the ANNs research in economics and finance.

Artificial Intelligence Review

2019 ◽  
pp. 23-39
Author(s):  
Amal Kilani ◽  
Ahmed Ben Hamida ◽  
Habib Hamam
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Intelligent System ◽  
Route Planning ◽  
Turing Test ◽  
Future Research ◽  
Interaction Data ◽  
Research Directions ◽  
Future Research Directions

In this chapter, the authors present a profound literature review of artificial intelligence (AI). After defining it, they briefly cover its history and enumerate its principal fields of application. They name, for example, information system, commerce, image processing, human-computer interaction, data compression, robotics, route planning, etc. Moreover, the test that defines an artificially intelligent system, called the Turing test, is also defined and detailed. Afterwards, the authors describe some AI tools such as fuzzy logic, genetic algorithms, and swarm intelligence. Special attention will be given to neural networks and fuzzy logic. The authors also present the future research directions and ethics.

Forecasting Stock Returns with Artificial Neural Networks

2011 ◽  
pp. 47-79 ◽  
Author(s):  
Suraphan Thawornwong ◽  
David Enke
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Stock Returns ◽  
Future Research ◽  
Network Architectures ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

During the last few years there has been growing literature on applications of artificial neural networks to business and financial domains. In fact, a great deal of attention has been placed in the area of stock return forecasting. This is due to the fact that once artificial neural network applications are successful, monetary rewards will be substantial. Many studies have reported promising results in successfully applying various types of artificial neural network architectures for predicting stock returns. This chapter reviews and discusses various neural network research methodologies used in 45 journal articles that attempted to forecast stock returns. Modeling techniques and suggestions from the literature are also compiled and addressed. The results show that artificial neural networks are an emerging and promising computational technology that will continue to be a challenging tool for future research.

scholarly journals Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico

Healthcare ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 181 ◽  
Author(s):  
Patricia Melin ◽  
Julio Cesar Monica ◽  
Daniela Sanchez ◽  
Oscar Castillo
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Fuzzy Logic ◽  
Network Models ◽  
Neural Network Models ◽  
Validation Data ◽  
Data Set ◽  
Ensemble Neural Networks ◽  
Ensemble Neural Network

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks. Fuzzy logic is then used to aggregate the responses of several predictor modules, in this way, improving the final prediction by combining the outputs of the modules in an intelligent way. Fuzzy logic handles the uncertainty in the process of making a final decision about the prediction. The complete model was tested for the case of predicting the COVID-19 time series in Mexico, at the level of the states and the whole country. The simulation results of the multiple ensemble neural network models with fuzzy response integration show very good predicted values in the validation data set. In fact, the prediction errors of the multiple ensemble neural networks are significantly lower than using traditional monolithic neural networks, in this way showing the advantages of the proposed approach.

Untangling Neural Nets

2004 ◽  
Vol 98 (2) ◽  
pp. 371-378 ◽  
Author(s):  
SCOTT DE MARCHI ◽  
CHRISTOPHER GELPI ◽  
JEFFREY D. GRYNAVISKI
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Logit Model ◽  
Model Building ◽  
Neural Nets ◽  
Security Studies ◽  
Future Research ◽  
Linear Discriminant ◽  
Nonparametric Approach ◽  
Traditional Approaches

Beck, King, and Zeng (2000) offer both a sweeping critique of the quantitative security studies field and a bold new direction for future research. Despite important strengths in their work, we take issue with three aspects of their research: (1) the substance of the logit model they compare to their neural network, (2) the standards they use for assessing forecasts, and (3) the theoretical and model-building implications of the nonparametric approach represented by neural networks. We replicate and extend their analysis by estimating a more complete logit model and comparing it both to a neural network and to a linear discriminant analysis. Our work reveals that neural networks do not perform substantially better than either the logit or the linear discriminant estimators. Given this result, we argue that more traditional approaches should be relied upon due to their enhanced ability to test hypotheses.

Application of Neural Networks and Fuzzy Logic to the Calibration of the Seven-Hole Probe

1998 ◽  
Vol 120 (1) ◽  
pp. 95-101 ◽  
Author(s):  
O. K. Rediniotis ◽  
G. Chrysanthakopoulos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Logic ◽  
Middle Layer ◽  
Multi Layer Perceptron ◽  
Fuzzy Logic Systems ◽  
Calibration Methods ◽  
Wide Range ◽  
Fuzzy Logic Approach ◽  
Logic Approach

The theory and techniques of Artificial Neural Networks (ANN) and Fuzzy Logic Systems (FLS) are applied toward the formulation of accurate and wide-range calibration methods for such flow-diagnostics instruments as multi-hole probes. Besides introducing new calibration techniques, part of the work’s objective is to: (a) apply fuzzy-logic methods to identify systems whose behavior is described in a “crisp” rather than a “linguistic” framework and (b) compare the two approaches, i.e., neural network versus fuzzy logic approach, and their potential as universal approximators. For the ANN approach, several network configurations were tried. A Multi-Layer Perceptron with a 2-node input layer, a 4-node output layer and a 7-node hidden/middle layer, performed the best. For the FLS approach, a system with center average defuzzifier, product-inference rule, singleton fuzzifier, and Gaussian membership functions was employed. The Fuzzy Logic System seemed to outperform the Neural Network/Multi-Layer Perceptron.

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