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.

Evolutionary Computing

2009 ◽  
pp. 131-142
Author(s):  
Thomas E. Potok ◽  
Xiaohui Cui ◽  
Yu Jiao
Keyword(s):  
Neural Networks ◽  
Evolutionary Algorithms ◽  
Swarm Intelligence ◽  
Real World ◽  
Artificial Life ◽  
Evolutionary Computing ◽  
New Paradigm ◽  
New Methods ◽  
Nature Inspired Computing ◽  
Real World Problems

The rate at which information overwhelms humans is significantly more than the rate at which humans have learned to process, analyze, and leverage this information. To overcome this challenge, new methods of computing must be formulated, and scientist and engineers have looked to nature for inspiration in developing these new methods. Consequently, evolutionary computing has emerged as new paradigm for computing, and has rapidly demonstrated its ability to solve real-world problems where traditional techniques have failed. This field of work has now become quite broad and encompasses areas ranging from artificial life to neural networks. This chapter specifically focuses on two sub-areas of nature-inspired computing: Evolutionary Algorithms and Swarm Intelligence.

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 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.

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.

Deep Learning and Biomedical Engineering

Biotechnology ◽  
2019 ◽  
pp. 562-575
Author(s):  
Suraj Sawant
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Deep Learning ◽  
Real World ◽  
Biomedical Engineering ◽  
Input Data ◽  
Reference Source ◽  
Data Input ◽  
Essential Information

Deep learning (DL) is a method of machine learning, as running over artificial neural networks, which has a structure above the standards to deal with large amounts of data. That is generally because of the increasing amount of data, input data sizes, and of course, greater complexity of objective real-world problems. Performed research studies in the associated literature show that the DL currently has a good performance among considered problems and it seems to be a strong solution for more advanced problems of the future. In this context, this chapter aims to provide some essential information about DL and its applications within the field of biomedical engineering. The chapter is organized as a reference source for enabling readers to have an idea about the relation between DL and biomedical engineering.

Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks

2017 ◽  
pp. 787-825
Author(s):  
Yevgeniy Bodyanskiy ◽  
Olena Vynokurova ◽  
Oleksii Tyshchenko
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Learning Process ◽  
Computational Intelligence ◽  
Group Method ◽  
Learning Criterion ◽  
Adjustment Procedure ◽  
Artificial Neural ◽  
Input Variables

This work is devoted to synthesis of adaptive hybrid systems based on the Computational Intelligence (CI) methods (especially artificial neural networks (ANNs)) and the Group Method of Data Handling (GMDH) ideas to get new qualitative results in Data Mining, Intelligent Control and other scientific areas. The GMDH-artificial neural networks (GMDH-ANNs) are currently well-known. Their nodes are two-input N-Adalines. On the other hand, these ANNs can require a considerable number of hidden layers for a necessary approximation quality. Introduced Q-neurons can provide a higher quality using the quadratic approximation. Their main advantage is a high learning rate. Universal approximating properties of the GMDH-ANNs can be achieved with the help of compartmental R-neurons representing a two-input RBFN with the grid partitioning of the input variables' space. An adjustment procedure of synaptic weights as well as both centers and receptive fields is provided. At the same time, Epanechnikov kernels (their derivatives are linear to adjusted parameters) can be used instead of conventional Gauss functions in order to increase a learning process rate. More complex tasks deal with stochastic time series processing. This kind of tasks can be solved with the help of the introduced adaptive W-neurons (wavelets). Learning algorithms are characterized by both tracking and smoothing properties based on the quadratic learning criterion. Robust algorithms which eliminate an influence of abnormal outliers on the learning process are introduced too. Theoretical results are illustrated by multiple experiments that confirm the proposed approach's effectiveness.

An enhanced method for the identification of ferritic morphologies in welded fusion zones based on gray-level co-occurrence matrix: A computational intelligence approach

2020 ◽  
pp. 095440622094226 ◽  
Author(s):  
Amanda Campos Souza ◽  
Gulliver Catão Silva ◽  
Lecino Caldeira ◽  
Fernando Marques de Almeida Nogueira ◽  
Moisés Luiz Lagares Junior ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Computational Intelligence ◽  
Low Carbon Steel ◽  
Support Vector ◽  
Gray Level ◽  
Low Carbon ◽  
Identification Rate ◽  
Artificial Neural ◽  
Occurrence Matrix

This work focuses on the identification of five of the most common ferritic morphologies present in welded fusion zones of low carbon steel through images acquired by photomicrographies. With this regards, we discuss the importance of the gray-level co-occurrence matrix to extract the features to be used as the input of the computational intelligence techniques. We use artificial neural networks and support vector machines to identify the proportions of each morphology and present the error identification rate for each technique. The results show that the use of gray-level co-occurrence extraction allows a less intense computational model with statistical validity and the support vector machine as a computational intelligence technique allows smaller variability when compared to the artificial neural networks.

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