Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Deterministic annealing EM algorithm
Neural Networks
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Advanced Methods in Neural Computing
Advanced Methods in Neural Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
SMEM Algorithm for Mixture Models
Neural Computation
Fuzzy neural networks for obstacle pattern recognition and collision avoidance of fish robots
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
An adaptive neuro-fuzzy system for efficient implementations
Information Sciences: an International Journal
An Efficient K-Hyperplane Clustering Algorithm and Its Application to Sparse Component Analysis
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
Neurofuzzy networks with nonlinear quantum learning
IEEE Transactions on Fuzzy Systems
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
Modeling and simulation of combinational CMOS logic circuits by ANFIS
Microelectronics Journal
Forecasting coal and rock dynamic disaster based on adaptive neuro-fuzzy inference system
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part II
Neural networks with quantum architecture and quantum learning
International Journal of Circuit Theory and Applications
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
A transformed input-domain approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Self-organized fuzzy system generation from training examples
IEEE Transactions on Fuzzy Systems
Designing fuzzy inference systems from data: An interpretability-oriented review
IEEE Transactions on Fuzzy Systems
Robust TSK fuzzy modeling for function approximation with outliers
IEEE Transactions on Fuzzy Systems
An input-output clustering approach to the synthesis of ANFIS networks
IEEE Transactions on Fuzzy Systems
Switching regression models and fuzzy clustering
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
Using Uncorrupted Neighborhoods of the Pixels for Impulsive Noise Suppression With ANFIS
IEEE Transactions on Image Processing
Median radial basis function neural network
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
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Adaptive neurofuzzy inference systems (ANFIS) represent an efficient technique for the solution of function approximation problems. When numerical samples are available in this regard, the synthesis of ANFIS networks can be carried out exploiting clustering algorithms. Starting from a hyperplane clustering synthesis in the joint input-output space, a computationally efficient optimization of ANFIS networks is proposed in this paper. It is based on a hierarchical constructive procedure, by which the number of rules is progressively increased and the optimal one is automatically determined on the basis of learning theory in order to maximize the generalization capability of the resulting ANFIS network. Extensive computer simulations prove the validity of the proposed algorithm and show a favorable comparison with other well-established techniques.