Result: A new design of polynomial neural networks in the framework of genetic algorithms

Title:
A new design of polynomial neural networks in the framework of genetic algorithms
Authors:
KIM, Dongwon, PARK, Gwi-Tae
Source:
IEICE transactions on information and systems. 89(8):2429-2438
Publisher Information:
Oxford: Oxford University Press, 2006.
Publication Year:
2006
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Electronique, Electronics, Circuits électriques, optiques et optoélectroniques, Electric, optical and optoelectronic circuits, Réseaux neuronaux, Neural networks, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Codage, codes, Coding, codes, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Codage binaire, Binary coding, Codificación binaria, Etude comparative, Comparative study, Estudio comparativo, Evaluation performance, Performance evaluation, Evaluación prestación, Fonction poids, Weight function, Función peso, Implémentation, Implementation, Implementación, Maniement donnée, Data handling, Manipulación dato, Mappage, Mapping, Carta de datos, Modélisation, Modeling, Modelización, Méthode itérative, Iterative method, Método iterativo, Noeud structure, Nodes, Nudo estructura, Pondération, Weighting, Ponderación, Réseau neuronal, Neural network, Red neuronal, binary coding, fitness function, genetic algorithm (GA), new design methodology, polynomial neural networks, weighting factor
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, Korea University, 1, 5-ka, Anam-dong, Seongbuk-ku, Seoul 136-701, Korea, Republic of
ISSN:
0916-8532
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18036996
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Electronics

Telecommunications and information theory
Accession Number:
edscal.18036996
Database:
PASCAL Archive

Further Information

We discuss a new design methodology of polynomial neural networks (PNN) in the framework of genetic algorithm (GA). The PNN is based on the ideas of group method of data handling (GMDH). Each node in the network is very flexible and can carry out polynomial type mapping between input and output variables. But the performances of PNN depend strongly on the number of input variables available to the model, the number of input variables, and the type (order) of the polynomials to each node. In this paper, GA is implemented to better use the optimal inputs and the order of polynomial in each node of PNN. The appropriate inputs and order are evolved accordingly and are tuned gradually throughout the GA iterations. We employ a binary coding for encoding key factors of the PNN into the chromosomes. The chromosomes are made of three sub-chromosomes which represent the order, number of inputs, and input candidates for modeling. To construct model by using significant approximation and generalization, we introduce the fitness function with a weighting factor. Comparisons with other modeling methods and conventional PNN show that the proposed design method offers encouraging advantages and better performance.