An empirical validation of software cost estimation models
Communications of the ACM
Universal approximation using radial-basis-function networks
Neural Computation
Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
COLT '97 Proceedings of the tenth annual conference on Computational learning theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computational Intelligence in Telecommunications Networks
Computational Intelligence in Telecommunications Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Nonlinear system modeling and robust predictive control based on RBF-ARX model
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Modelling of electrostatic fluidized bed (EFB) coating process using artificial neural networks
Engineering Applications of Artificial Intelligence
A neural network-assisted finite element analysis of cold flat rolling
Engineering Applications of Artificial Intelligence
Dual heuristic programming based nonlinear optimal control for a synchronous generator
Engineering Applications of Artificial Intelligence
Neural network based tire/road friction force estimation
Engineering Applications of Artificial Intelligence
Neural network model-based automotive engine air/fuel ratio control and robustness evaluation
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
Statistical inference in a redesigned Radial Basis Function neural network
Engineering Applications of Artificial Intelligence
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To realize effective modeling and secure accurate prediction abilities of models for power supply for high-field magnet (PSHFM), we develop a comprehensive design methodology of information granule-oriented radial basis function (RBF) neural networks. The proposed network comes with a collection of radial basis functions, which are structurally as well as parametrically optimized with the aid of information granulation and genetic algorithm. The structure of the information granule-oriented RBF neural networks invokes two types of clustering methods such as K-Means and fuzzy C-Means (FCM). The taxonomy of the resulting information granules relates to the format of the activation functions of the receptive fields used in RBF neural networks. The optimization of the network deals with a number of essential parameters as well as the underlying learning mechanisms (e.g., the width of the Gaussian function, the numbers of nodes in the hidden layer, and a fuzzification coefficient used in the FCM method). During the identification process, we are guided by a weighted objective function (performance index) in which a weight factor is introduced to achieve a sound balance between approximation and generalization capabilities of the resulting model. The proposed model is applied to modeling power supply for high-field magnet where the model is developed in the presence of a limited dataset (where the small size of the data is implied by high costs of acquiring data) as well as strong nonlinear characteristics of the underlying phenomenon. The obtained experimental results show that the proposed network exhibits high accuracy and generalization capabilities.