Expert Systems with Applications: An International Journal
A nature inspired Ying-Yang approach for intelligent decision support in bank solvency analysis
Expert Systems with Applications: An International Journal
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
HebbR2-Taffic: A novel application of neuro-fuzzy network for visual based traffic monitoring system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A fuzzy neural network with fuzzy impact grades
Neurocomputing
Efficient Parametric Adjustment of Fuzzy Inference System Using Error Backpropagation Method
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
Expert Systems with Applications: An International Journal
Efficient parametric adjustment of fuzzy inference system using unconstrained optimization
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
eFSM: a novel online neural-fuzzy semantic memory model
IEEE Transactions on Neural Networks
A probabilistic fuzzy approach to modeling nonlinear systems
Neurocomputing
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Neural fuzzy networks proposed in the literature can be broadly classified into two groups. The first group is essentially fuzzy systems with self-tuning capabilities and requires an initial rule base to be specified prior to training. The second group of neural fuzzy networks, on the other hand, is able to automatically formulate the fuzzy rules from the numerical training data. Examples are the Falcon-ART, and the POPFNN family of networks. A cluster analysis is first performed on the training data and the fuzzy rules are subsequently derived through the proper connections of these computed clusters. This correspondence proposes two new networks: Falcon-FKP and Falcon-PFKP. They are extensions of the Falcon-ART network, and aimed to overcome the shortcomings faced by the Falcon-ART network itself, i.e., poor classification ability when the classes of input data are very similar to each other, termination of training cycle depends heavily on a preset error parameter, the fuzzy rule base of the Falcon-ART network may not be consistent Nauck, there is no control over the number of fuzzy rules generated, and learning efficiency may deteriorate by using complementarily coded training data. These deficiencies are essentially inherent to the fuzzy ART, clustering technique employed by the Falcon-ART network. Hence, two clustering techniques- Fuzzy Kohonen Partitioning (FKP) and its pseudo variant PFKP, are synthesized with the basic Falcon structure to compute the fuzzy sets and to automatically derive the fuzzy rules from the training data. The resultant neural fuzzy networks are Falcon-FKP and Falcon-PFKP, respectively. These two proposed networks have a lean and efficient training algorithm and consistent fuzzy rule bases. Extensive simulations are conducted using the two networks and their performances are encouraging when benchmarked against other neural and neural fuzzy systems.