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Treffer: Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network

Title:
Intelligent forecasting system based on integration of electromagnetism-like mechanism and fuzzy neural network
Authors:
PEITSANG WU, HUNG, Yung-Yao, LIN, Zi-Po
Source:
Expert systems with applications. 41(6):2660-2677
Publisher Information:
Amsterdam: Elsevier, 2014.
Publication Year:
2014
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Connexionnisme. Réseaux neuronaux, Connectionism. Neural networks, Algorithme EM, EM algorithm, Algoritmo EM, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Architecture réseau, Network architecture, Arquitectura red, Attraction, Atracción, Classification hiérarchique, Hierarchical classification, Clasificación jerarquizada, Descente gradient, Gradient descent, Gradient bajada, Electromagnétisme, Electromagnetism, Electromagnetismo, Equation évolution, Evolution equation, Ecuación evolución, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Intégration, Integration, Integración, Logique floue, Fuzzy logic, Lógica difusa, Modélisation, Modeling, Modelización, Métamodèle, Metamodel, Metamodelo, Méthode descente, Descent method, Método descenso, Méthode gradient, Gradient method, Método gradiente, Méthode heuristique, Heuristic method, Método heurístico, Optimisation, Optimization, Optimización, Optimum local, Local optimum, Optimo local, Prévision, Forecasting, Previsión, Redondance, Redundancy, Redundancia, Réseau neuronal, Neural network, Red neuronal, Rétropropagation, Backpropagation, Retropropagacíon, Système expert, Expert system, Sistema experto, Système intelligent, Intelligent system, Sistema inteligente, Ingénierie système, Systems engineering, Ingeniería de Sistemas, Réseau neuronal flou, Fuzzy neural nets, Red neuronal difusa, Back propagation algorithm, Electromagnetism-like mechanism, Fuzzy neural networks, Weights elimination
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Department of Industrial Management, I-Shou University, Kaohsiung 840, Tawain, Province of China
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28296074
Rights:
Copyright 2015 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
Accession Number:
edscal.28296074
Database:
PASCAL Archive

Weitere Informationen

Fuzzy neural network (FNN) architectures, in which fuzzy logic and artificial neural networks are integrated, have been proposed by many researchers. In addition to developing the architecture for the FNN models, evolution of the learning algorithms for the connection weights is also a very important. Researchers have proposed gradient descent methods such as the back propagation algorithm and evolution methods such as genetic algorithms (GA) for training FNN connection weights. In this paper, we integrate a new meta-heuristic algorithm, the electromagnetism-like mechanism (EM), into the FNN training process. The EM algorithm utilizes an attraction-repulsion mechanism to move the sample points towards the optimum. However, due to the characteristics of the repulsion mechanism, the EM algorithm does not settle easily into the local optimum. We use EM to develop an EM-based FNN (the EM-initialized FNN) model with fuzzy connection weights. Further, the EM-initialized FNN model is used to train fuzzy if-then rules for learning expert knowledge. The results of comparisons done of the performance of our EM-initialized FNN model to conventional FNN models and GA-initialized FNN models proposed by other researchers indicate that the performance of our EM-initialized FNN model is better than that of the other FNN models. In addition, our use of a fuzzy ranking method to eliminate redundant fuzzy connection weights in our FNN architecture results in improved performance over other FNN models.