An accurate COG Defuzzifier design using Lamarckian co-adaptation of learning and evolution
Fuzzy Sets and Systems - Fuzzy models
Evolutionary Training of Neuro-fuzzy Patches for Function Approximation
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Granularity and specificity in fuzzy rule-based systems
Granular computing
A method for fuzzy system identification based on clustering analysis
Systems Analysis Modelling Simulation
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Fuzzy Modeling Based on Ordinary Fuzzy Partitions and Nearest Neighbor Clustering
Journal of Intelligent and Robotic Systems
A Fuzzy-Logic Mapper for Audiovisual Media
Computer Music Journal
International Journal of Approximate Reasoning
A self-organizing feature map-driven approach to fuzzy approximate reasoning
Expert Systems with Applications: An International Journal
Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
A novel hybrid algorithm for function approximation
Expert Systems with Applications: An International Journal
Efficient and interpretable fuzzy classifiers from data with support vector learning
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Feedforward neural networks training with optimal bounded ellipsoid algorithm
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Potential Assessment of an Ellipsoidal Neural Fuzzy Time Series Model for Freeway Traffic Prediction
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
Neural Networks Training with Optimal Bounded Ellipsoid Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Fewer Hyper-Ellipsoids Fuzzy Rules Generation Using Evolutional Learning Scheme
Cybernetics and Systems
Data-driven fuzzy clustering based on maximum entropy principle and PSO
Expert Systems with Applications: An International Journal
WSEAS Transactions on Computers
Hybrid robust approach for TSK fuzzy modeling with outliers
Expert Systems with Applications: An International Journal
Recurrent neural networks training with stable bounding ellipsoid algorithm
IEEE Transactions on Neural Networks
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Adaptive fuzzy approach to function approximation with PSO and RLSE
Expert Systems with Applications: An International Journal
Genetically dynamic optimization based fuzzy polynomial neural networks
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
Genetically optimized fuzzy polynomial neural networks with fuzzy set-based polynomial neurons
Information Sciences: an International Journal
FSPN-Based genetically optimized fuzzy polynomial neural networks
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
Fuzzy linear regression based on Polynomial Neural Networks
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
A fuzzy rule can have the shape of an ellipsoid in the input-output state spare of a system. Then an additive fuzzy system approximates a function by covering its graph with ellipsoidal rule patches. It averages rule patches that overlap. The best fuzzy rules cover the extrema or bumps in the function. Neural or statistical clustering systems can approximate the unknown fuzzy rules from training data. Neural systems can then both tune these rules and add rules to improve the function approximation. We use a hybrid neural system that combines unsupervised and supervised learning to find and tune the rules in the form of ellipsoids. Unsupervised competitive learning finds the first-order and second-order statistics of clusters in the training data. The covariance matrix of each cluster gives an ellipsoid centered at the vector or centroid of the data cluster. The supervised neural system learns with gradient descent. It locally minimizes the mean-squared error of the fuzzy function approximation. In the hybrid system unsupervised learning initializes the gradient descent. The hybrid system tends to give a more accurate function approximation than does the lone unsupervised or supervised system. We found a closed-form model for the optimal rules when only the centroids of the ellipsoids change. We used numerical techniques to find the optimal rules in the general case