Artificial Neural Networks Used for Development Prediction of State-of-the-Art Surface Engineering Areas

2015 ◽  
Vol 220-221 ◽  
pp. 785-789
Author(s):  
Anna Danuta Dobrzańska-Danikiewicz ◽  
Jacek Trzaska ◽  
Agnieszka Sękala ◽  
Adam Jagiełło
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Models ◽  
Surface Engineering ◽  
Materials Science ◽  
Added Value ◽  
Future Research ◽  
Processing Technologies ◽  
Future Events ◽  
Artificial Neural

The paper presents new, possible applications of artificial neural networks in the field of materials science and material engineering in relation to other artificial intelligence methods known and applied in this area. The most recent simulation experiments, the exemplary results of which are presented in this paper, point out that the scope of the existing applications of artificial neural networks can be extended to encompass new areas related to prediction of development of materials treatment and processing technologies. The goal of such research is to focus, intentionally, the areas of future research and investments on the most promising areas likely to yield the highest added value in the future together with mitigating a risk relating to such a process. The computational models created were used for creating multi-variant probabilistic scenarios of future events based on heuristic independent variables acquired in the process of multi-stage expert surveys. Dependencies were determined, in particular, between the probability of occurrence of alternative macro-scenarios of future events and the development of the relevant thematic areas of M1–M7 and P1–P7.

scholarly journals Crystal-Site-Based Artificial Neural Networks for Material Classification

Crystals ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1039
Author(s):  
Juan I. Gómez-Peralta ◽  
Nidia G. García-Peña ◽  
Xim Bokhimi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Crystal Structures ◽  
Materials Science ◽  
Layered Perovskite ◽  
Structure Property ◽  
Average Precision ◽  
Artificial Neural ◽  
Spinel Structures ◽  
Crystal Compound

In materials science, crystal structures are the cornerstone in the structure–property paradigm. The description of crystal compounds may be ascribed to the number of different atomic chemical environments, which are related to the Wyckoff sites. Hence, a set of features related to the different atomic environments in a crystal compound can be constructed as input data for artificial neural networks (ANNs). In this article, we show the performance of a series of ANNs developed using crystal-site-based features. These ANNs were developed to classify compounds into halite, garnet, fluorite, hexagonal perovskite, ilmenite, layered perovskite, -o-tp- perovskite, perovskite, and spinel structures. Using crystal-site-based features, the ANNs were able to classify the crystal compounds with a 93.72% average precision. Furthermore, the ANNs were able to retrieve missing compounds with one of these archetypical structure types from a database. Finally, we showed that the developed ANNs were also suitable for a multitask learning paradigm, since the extracted information in the hidden layers linearly correlated with lattice parameters of the crystal structures.

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 Computational Principles of Supervised Learning in the Cerebellum

2018 ◽  
Vol 41 (1) ◽  
pp. 233-253 ◽  
Author(s):  
Jennifer L. Raymond ◽  
Javier F. Medina
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Supervised Learning ◽  
Brain Area ◽  
Future Research ◽  
Brain Areas ◽  
Adaptive Mechanisms ◽  
Networks Analysis ◽  
Artificial Neural ◽  
Organizational Principles

Supervised learning plays a key role in the operation of many biological and artificial neural networks. Analysis of the computations underlying supervised learning is facilitated by the relatively simple and uniform architecture of the cerebellum, a brain area that supports numerous motor, sensory, and cognitive functions. We highlight recent discoveries indicating that the cerebellum implements supervised learning using the following organizational principles: ( a) extensive preprocessing of input representations (i.e., feature engineering), ( b) massively recurrent circuit architecture, ( c) linear input–output computations, ( d) sophisticated instructive signals that can be regulated and are predictive, ( e) adaptive mechanisms of plasticity with multiple timescales, and ( f) task-specific hardware specializations. The principles emerging from studies of the cerebellum have striking parallels with those in other brain areas and in artificial neural networks, as well as some notable differences, which can inform future research on supervised learning and inspire next-generation machine-based algorithms.

Artificial Neural Networks in Materials Science Application

2010 ◽  
Vol 20-23 ◽  
pp. 1211-1216 ◽  
Author(s):  
Wen Yu Zhang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Materials Science ◽  
Material Design ◽  
Widespread Application ◽  
Plastic Processing ◽  
Artificial Neural ◽  
Science Application ◽  
Compound Materials ◽  
Material Preparation

Because but the artificial neural networks has the strong non-linear problem handling ability also the fault tolerance strong obtains the widespread application in the materials science.This article to its material design, the material preparation craft optimizes, the plastic processing, the heat treatment, the compound materials, corrode, domain and so on casting applications have carried on the discussion.

FORECASTING FOREIGN EXCHANGE RATES WITH ARTIFICIAL NEURAL NETWORKS: A REVIEW

2004 ◽  
Vol 03 (01) ◽  
pp. 145-165 ◽  
Author(s):  
WEI HUANG ◽  
K. K. LAI ◽  
Y. NAKAMORI ◽  
SHOUYANG WANG
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rates ◽  
Network Architecture ◽  
Future Research ◽  
Promising Alternative ◽  
Practical Applications ◽  
Future Research Directions ◽  
Artificial Neural ◽  
Input Variables

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed.

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

scholarly journals Exploring Correlations Between Properties Using Artificial Neural Networks

2019 ◽  
Vol 51 (1) ◽  
pp. 58-75
Author(s):  
Yiming Zhang ◽  
Julian R. G. Evans ◽  
Shoufeng Yang
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Materials Science ◽  
Causal Relationships ◽  
Structure And Properties ◽  
Compositional Field ◽  
Artificial Neural ◽  
Design Materials

Abstract The traditional aim of materials science is to establish the causal relationships between composition, processing, structure, and properties with the intention that, eventually, these relationships will make it possible to design materials to meet specifications. This paper explores another approach. If properties are related to structure at different scales, there may be relationships between properties that can be discerned and used to make predictions so that knowledge of some properties in a compositional field can be used to predict others. We use the physical properties of the elements as a dataset because it is expected to be both extensive and reliable and we explore this method by showing how it can be applied to predict the polarizability of the elements from other properties.

Evolutionary Approaches for ANNs Design

2011 ◽  
pp. 575-580
Author(s):  
Antonia Azzini ◽  
Andrea G.B. Tettamanzi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Algorithms ◽  
Computational Models ◽  
Artificial Neurons ◽  
Important Research Topic ◽  
Recent Developments ◽  
Evolving System ◽  
Artificial Neural ◽  
Ann Application

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.

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