Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Generating fuzzy membership functions: a monotonic neural network model
Fuzzy Sets and Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Induction of fuzzy rules and membership functions from training examples
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Fuzzy Modeling for Control
Generalized Version Space Learning Algorithm for Noisy and Uncertain Data
IEEE Transactions on Knowledge and Data Engineering
Learning fuzzy inference systems using an adaptive membership function scheme
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new method for constructing membership functions and fuzzy rulesfrom training examples
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Extracting fuzzy control rules from experimental human operatordata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Function approximation based on fuzzy rules extracted frompartitioned numerical data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An efficient fuzzy classifier with feature selection based on fuzzyentropy
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stable adaptive control using fuzzy systems and neural networks
IEEE Transactions on Fuzzy Systems
Neuro-fuzzy hybrid control system of tank level in petroleum plant
IEEE Transactions on Fuzzy Systems
A fuzzy clustering-based rapid prototyping for fuzzy rule-based modeling
IEEE Transactions on Fuzzy Systems
Convex-set-based fuzzy clustering
IEEE Transactions on Fuzzy Systems
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Extracting M-of-N rules from trained neural networks
IEEE Transactions on Neural Networks
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
A PSO-aided neuro-fuzzy classifier employing linguistic hedge concepts
Expert Systems with Applications: An International Journal
Adaptive Fuzzy Association Rule mining for effective decision support in biomedical applications
International Journal of Data Mining and Bioinformatics
A new adaptive fuzzy controller with saturation employing influential rule search scheme (IRSS)
International Journal of Knowledge-based and Intelligent Engineering Systems
OWA-weighted based clustering method for classification problem
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An information theoretic approach to generating fuzzy hypercubes for if-then classifiers
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Breast Cancer Classification Based on Advanced Multi Dimensional Fuzzy Neural Network
Journal of Medical Systems
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Automatic generation of fuzzy rule base and membership functions from an input-output data set, for reliable construction of an adaptive fuzzy inference system, has become an important area of research interest. The present paper proposes a new robust, fast acting adaptive fuzzy pattern classification scheme, named influential rule search scheme (IRSS). In IRSS, rules which are most influential in contributing to the error produced by the adaptive fuzzy system are identified at the end of each epoch and subsequently modified for satisfactory performance. This fuzzy rule base adjustment scheme is accompanied by an output membership function adaptation scheme for fine tuning the fuzzy system architecture. This iterative method has shown a relatively high speed of convergence. Performance of the proposed IRSS is compared with other existing pattern classification schemes by implementing it for Fisher's iris data problem and Wisconsin breast cancer data problems.