A self-organizing feature map-driven approach to fuzzy approximate reasoning
Expert Systems with Applications: An International Journal
Time sequence data mining using time-frequency analysis and soft computing techniques
Applied Soft Computing
Extracting symbolic knowledge from recurrent neural networks---A fuzzy logic approach
Fuzzy Sets and Systems
Pulse images recognition using fuzzy neural network
Expert Systems with Applications: An International Journal
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
An investigation of neuro-fuzzy systems in psychosomatic disorders
Expert Systems with Applications: An International Journal
Simulated annealing based pattern classification
Information Sciences: an International Journal
Analysis of artificial neural network learning near temporary minima: A fuzzy logic approach
Fuzzy Sets and Systems
Fuzzy rough granular neural networks, fuzzy granules, and classification
Theoretical Computer Science
Class-dependent rough-fuzzy granular space, dispersion index and classification
Pattern Recognition
A cluster-assumption based batch mode active learning technique
Pattern Recognition Letters
Time---domain non-linear feature parameter for consonant classification
International Journal of Speech Technology
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
Computers and Industrial Engineering
Explicit rough-fuzzy pattern classification model
Pattern Recognition Letters
Optimizing biodiversity prediction from abiotic parameters
Environmental Modelling & Software
| Hi-index | 0.01 |
A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and other related models