Universal approximation using radial-basis-function networks
Neural Computation
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Automatica (Journal of IFAC)
The nature of statistical learning theory
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Learning and generalization in radial basis function networks
Neural Computation
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Neural Processing Letters
A global learing algorithm for a RBF network
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Artificial Neural Networks for Modelling and Control of Non-Linear Systems
Hybrid Feedforward Neural Networks for Solving Classification Problems
Neural Processing Letters
Training Product Unit Neural Networks with Genetic Algorithms
IEEE Expert: Intelligent Systems and Their Applications
Machine Learning
Evolutionary product unit based neural networks for regression
Neural Networks
An analysis of diversity measures
Machine Learning
JCLEC: a Java framework for evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (pp 315-357) "Ordered structures in many-valued logic"
Regularization in the selection of radial basis function centers
Neural Computation
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Evolutionary product-unit neural networks classifiers
Neurocomputing
Hybridization of evolutionary algorithms and local search by means of a clustering method
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Pattern classification using neural networks
IEEE Communications Magazine
A new evolutionary system for evolving artificial neural networks
IEEE Transactions on Neural Networks
Effects of moving the center's in an RBF network
IEEE Transactions on Neural Networks
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
IEEE Transactions on Neural Networks
A general regression neural network
IEEE Transactions on Neural Networks
On global-local artificial neural networks for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An evolutionary algorithm that constructs recurrent neural networks
IEEE Transactions on Neural Networks
Classification by evolutionary generalised radial basis functions
International Journal of Hybrid Intelligent Systems - Advances in Intelligent Agent Systems
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part III
Expert Systems with Applications: An International Journal
Hybrid artificial neural networks: models, algorithms and data
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Evolutionary q-gaussian radial basis functions for binary-classification
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part II
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Early experiments with neural diversity machines
Neurocomputing
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This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three different combined basis function models are proposed with all the different pairs that can be obtained with SU, PU and RBF nodes: product-sigmoidal unit (PSU) neural networks, product-radial basis function (PRBF) neural networks, and sigmoidal-radial basis function (SRBF) neural networks; and these are compared to the corresponding pure models: product unit neural network (PUNN), multilayer perceptron (MLP) and the RBF neural network. The proposals are tested using ten benchmark classification problems from well known machine learning problems. Combined functions using projection and kernel functions are found to be better than pure basis functions for the task of classification in several datasets.