Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
POPFNN: a pseudo outer-product based fuzzy neural network
Neural Networks
Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules
Pattern Recognition Letters
Foundations of Neuro-Fuzzy Systems
Foundations of Neuro-Fuzzy Systems
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Artificial Intelligence in Medicine
Product Demand Forecasting with a Novel Fuzzy CMAC
Neural Processing Letters
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
IEEE Transactions on Neural Networks
A novel brain-inspired neural cognitive approach to SARS thermal image analysis
Expert Systems with Applications: An International Journal
FUZZ-IEEE'09 Proceedings of the 18th international conference on Fuzzy Systems
Supervised Pseudo Self-Evolving Cerebellar algorithm for generating fuzzy membership functions
Expert Systems with Applications: An International Journal
Cultural dependency analysis for understanding speech emotion
Expert Systems with Applications: An International Journal
SoHyFIS-Yager: A self-organizing Yager based Hybrid neural Fuzzy Inference System
Expert Systems with Applications: An International Journal
eT2FIS: An Evolving Type-2 Neural Fuzzy Inference System
Information Sciences: an International Journal
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Generally, clustering techniques may be classified into hierarchical-based and partition-based techniques. Hierarchical-based clustering techniques included single link [1], complete link [2] and [3] [4]. The main drawback of hierarchical clustering is that it is static, and points committed to a given cluster in the early stages cannot be moved to a different cluster. Prototype-based partition clustering techniques, on the other hand, are dynamic and the data points can move from one cluster to another under varying conditions. However, partition-based clustering techniques require prior knowledge such as the number of classes, C, in the set of training data. Such information may be unknown and is difficult to estimate in data sets such as traffic flow data [5]. For tasks such as the 2-Spiral problem [6], computing a predefined number of clusters, C, may not be good enough to satisfactorily solve the tasks. Moreover, partition-based clustering techniques suffer from the stability-plasticity dilemma [7] where new information cannot be learned without running the risk of eroding old (previously learned) but valid knowledge. Therefore, in the context of neural fuzzy systems [8] such as POPFNN [9], hierarchical clustering violates the networks' ability to self-organize and self-adapt with changing environments while current partition-based clustering techniques have some significant shortcomings. These deficiencies serve as the main motivations behind the development of the Discrete Incremental Clustering (DIC) technique. The DIC technique is not limited by the need to have prior knowledge of the number of clusters C and it preserves the dynamism of partition-based clustering techniques. The proposed DIC technique is implemented in a new neural fuzzy network named Gen-SoFNN [10] to demonstrate its performance.