scholarly journals Application of Artificial Neural Networks on improving predictions of nuclear radii

2019 ◽  
Vol 26 ◽  
pp. 179
Author(s):  
V. Skarlis ◽  
E. Bratsolis ◽  
E. Charou ◽  
T. J. Mertzimekis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Neural Systems ◽  
Artificial Neural ◽  
Nonlinear Nature ◽  
Real World Problems

Artificial Neural Networks (ANN) are mathematical computing paradigms imitating the operations of biological neural systems. Their nonlinear nature and ability to learn from the environment make them highly suited to solve real-world problems from those that are still under development. In the field of Physics there are many problems which cannot be adequately solved with the physics–based methods and the use of ANN may yield better results. In the present work ANNs have been tested in predicting nuclear radii considering as input the atomic and mass numbers, exclusively. The performance of different supervised ANNs is evaluated. The dataset used for the training and testing was based on evaluated data of nuclear radii available in IAEA tables.

Development of ANN with Adaptive Connections by CE

2006 ◽  
pp. 71-93
Author(s):  
Julián Dorado ◽  
Nieves Pedreira ◽  
Mónica Miguelez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Evolutionary Computation ◽  
Real World ◽  
Development Process ◽  
Multilevel System ◽  
Processing Elements ◽  
Artificial Neural ◽  
Real World Problems

This chapter presents the use of Artificial Neural Networks (ANN) and Evolutionary Computation (EC) techniques to solve real-world problems including those with a temporal component. The development of the ANN maintains some problems from the beginning of the ANN field that can be palliated applying EC to the development of ANN. In this chapter, we propose a multilevel system, based on each level in EC, to adjust the architecture and to train ANNs. Finally, the proposed system offers the possibility of adding new characteristics to the processing elements (PE) of the ANN without modifying the development process. This characteristic makes possible a faster convergence between natural and artificial neural networks.

Simultaneous Evolution of Network Architectures and Connection Weights in Artificial Neural Networks

2006 ◽  
pp. 28-42
Author(s):  
Ruhul A. Sarker ◽  
Hussein A. Abbass
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiobjective Optimization ◽  
Real World ◽  
Network Architectures ◽  
Research Areas ◽  
Artificial Neural ◽  
Multiobjective Algorithms ◽  
Simultaneous Evolution ◽  
Real World Problems

Artificial Neural Networks (ANNs) have become popular among researchers and practitioners for modeling complex real-world problems. One of the latest research areas in this field is evolving ANNs. In this chapter, we investigate the simultaneous evolution of network architectures and connection weights in ANNs. In simultaneous evolution, we use the well-known concept of multiobjective optimization and subsequently evolutionary multiobjective algorithms to evolve ANNs. The results are promising when compared with the traditional ANN algorithms. It is expected that this methodology would provide better solutions to many applications of ANNs.

scholarly journals INDUSTRIAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS

2014 ◽  
pp. 8-20
Author(s):  
Kurosh Madani
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Industrial Applications ◽  
Large Field ◽  
Nonlinear Functions ◽  
The Past ◽  
Real World Applications ◽  
Ann Models ◽  
Artificial Neural

In a large number of real world dilemmas and related applications the modeling of complex behavior is the central point. Over the past decades, new approaches based on Artificial Neural Networks (ANN) have been proposed to solve problems related to optimization, modeling, decision making, classification, data mining or nonlinear functions (behavior) approximation. Inspired from biological nervous systems and brain structure, Artificial Neural Networks could be seen as information processing systems, which allow elaboration of many original techniques covering a large field of applications. Among their most appealing properties, one can quote their learning and generalization capabilities. The main goal of this paper is to present, through some of main ANN models and based techniques, their real application capability in real world industrial dilemmas. Several examples through industrial and real world applications have been presented and discussed.

scholarly journals Constraints of Biological Neural Networks and Their Consideration in AI Applications

2010 ◽  
Vol 2010 ◽  
pp. 1-6 ◽  
Author(s):  
Richard Stafford
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Physical Properties ◽  
Ecological Niche ◽  
Neural Systems ◽  
Ecological Constraints ◽  
Process Information ◽  
Spiking Networks ◽  
Artificial Neural ◽  
Biological Organisms

Biological organisms do not evolve to perfection, but to out compete others in their ecological niche, and therefore survive and reproduce. This paper reviews the constraints imposed on imperfect organisms, particularly on their neural systems and ability to capture and process information accurately. By understanding biological constraints of the physical properties of neurons, simpler and more efficient artificial neural networks can be made (e.g., spiking networks will transmit less information than graded potential networks, spikes only occur in nature due to limitations of carrying electrical charges over large distances). Furthermore, understanding the behavioural and ecological constraints on animals allows an understanding of the limitations of bio-inspired solutions, but also an understanding of why bio-inspired solutions may fail and how to correct these failures.

Copulas-based ensemble of Artificial Neural Networks for forecasting real world time series

2016 ◽  
Author(s):  
Ricardo T. A. de Oliveira ◽  
Thaize Fernandes O. de Assis ◽  
Paulo Renato A. Firmino ◽  
Tiago A. E. Ferreira ◽  
Adriano L. I. Oliveira
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Real World ◽  
Artificial Neural

EDITORIAL

2002 ◽  
Vol 16 (07) ◽  
pp. 773-779 ◽  
Author(s):  
NIKHIL R. PAL ◽  
SRIMANTA PAL
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Computational Intelligence ◽  
Rough Sets ◽  
Artificial Life ◽  
Considerable Potential ◽  
Intelligence System ◽  
Intelligent Behavior ◽  
Real World Problems

Irrespective of the way computational intelligence (CI) is defined, its components should have the following characteristics: considerable potential in solving real world problems, ability to learn from experience, capability of self-organizing, and ability of adapting in response to dynamically changing conditions and constraints. To summarize, it should display aspects of intelligent behavior as observed in humans. In view of these, we assume that the major ingredients of a computational intelligence system are artificial neural networks, fuzzy sets, rough sets, and evolutionary computation. Some other components that may be parts of computational intelligence (CI) systems are artificial life and immuno computing. It is a synergistic combination of all these components.

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