Extended hedge algebras and their application to fuzzy logic
Fuzzy Sets and Systems
A centered formulation of Takagi-Sugeno rules for improved learning efficiency
Fuzzy Sets and Systems
A neuro-fuzzy method to learn fuzzy classification rules from data
Fuzzy Sets and Systems - Special issue: application of neuro-fuzzy systems
Fuzzy evolutionary computation
Fuzzy evolutionary computation
A genetic algorithm for optimizing Takagi-Sugeno fuzzy rule bases
Fuzzy Sets and Systems
A genetic-algorithm-based method for tuning fuzzy logic controllers
Fuzzy Sets and Systems
Processing individual fuzzy attributes for fuzzy rule induction
Fuzzy Sets and Systems
Genetic programming for model selection of TSK-fuzzy systems
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Three objective genetics-based machine learning for linguisitc rule extraction
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Influential Rule Search Scheme (IRSS)-A New Fuzzy Pattern Classifier
IEEE Transactions on Knowledge and Data Engineering
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Implementation of evolutionary fuzzy systems
IEEE Transactions on Fuzzy Systems
SLAVE: a genetic learning system based on an iterative approach
IEEE Transactions on Fuzzy Systems
GA-fuzzy modeling and classification: complexity and performance
IEEE Transactions on Fuzzy Systems
Compact and transparent fuzzy models and classifiers through iterative complexity reduction
IEEE Transactions on Fuzzy Systems
Fuzzy control of pH using genetic algorithms
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
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
Constructing accurate fuzzy classifiers: A new adaptive method for rule-weight specification
International Journal of Knowledge-based and Intelligent Engineering Systems
Expert Systems with Applications: An International Journal
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Improving fuzzy knowledge integration with particle swarmoptimization
Expert Systems with Applications: An International Journal
Design of real-time fuzzy bus holding system for the mass rapid transit transfer system
Expert Systems with Applications: An International Journal
Review Article: Applications of neuro fuzzy systems: A brief review and future outline
Applied Soft Computing
Hi-index | 12.06 |
The present paper proposes the development of an adaptive neuro-fuzzy classifier which employs two relatively less explored and comparatively new problem solving domains in fuzzy systems. The relatively less explored field is the domain of the fuzzy linguistic hedges which has been employed here to define the flexible shapes of the fuzzy membership functions (MFs). To achieve finer and finer adaptation, and hence control, over the fuzzy MFs, each MF is composed of several piecewise MF sections and the shape of each such MF section is varied by applying a fuzzy linguistic operator on it. The system employs a Takagi-Sugeno based neuro-fuzzy system where the rule consequences are described by zero order elements. This proposed linguistic hedge based neuro-fuzzy classifier (LHBNFC) employs a relatively new field in the area of combinatorial metaheuristics, called particle swarm optimization (PSO), for its efficient learning. PSO has been employed in this scheme to simultaneously tune the shape of the fuzzy MFs as well as the rule consequences for the entire fuzzy rule base. The performance of the proposed system is demonstrated by implementing it for two classical benchmark data sets: (i) the iris data and (ii) the thyroid data. Performance comparison vis-a-vis other available algorithms shows the effectiveness of our proposed algorithm.