Universal approximation using radial-basis-function networks
Neural Computation
Estimating software costs
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Software Engineering Economics
Software Engineering Economics
Software Cost Estimation with Cocomo II with Cdrom
Software Cost Estimation with Cocomo II with Cdrom
The design of self-organizing polynomial neural networks
Information Sciences—Informatics and Computer Science: An International Journal
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Selecting Best Practices for Effort Estimation
IEEE Transactions on Software Engineering
Refinement of temporal constraints in fuzzy associations
International Journal of Approximate Reasoning
Fast learning in networks of locally-tuned processing units
Neural Computation
A methodology for automated fuzzy model generation
Fuzzy Sets and Systems
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
Adaptive fuzzy fitness granulation for evolutionary optimization
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning
Genetic learning of fuzzy rules based on low quality data
Fuzzy Sets and Systems
Linguistic models as a framework of user-centric system modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Neuro-fuzzy rule generation: survey in soft computing framework
IEEE Transactions on Neural Networks
Using radial basis functions to approximate a function and its error bounds
IEEE Transactions on Neural Networks
Evolving space-filling curves to distribute radial basis functions over an input space
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Radial basis function network using intuitionistic fuzzy C means for software cost estimation
International Journal of Computer Applications in Technology
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In this study, we introduce a new design methodology of fuzzy radial basis function-based polynomial neural networks. In many cases, these models do not come with capabilities to deal with granular information. With this regard, fuzzy sets offer several interesting and useful opportunities. This study presents the development of fuzzy radial basis function-based neural networks augmented with virtual input variables. The performance of the proposed category of models is quantified through a series of experiments, in which we use two machine learning data sets and two publicly available software development effort data.