Cascade Architectures of Fuzzy Neural Networks
Fuzzy Optimization and Decision Making
On-line Writer Adaptation for Handwriting Recognition using Fuzzy Inference Systems
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A Fuzzy-Logic Mapper for Audiovisual Media
Computer Music Journal
A fuzzy decision tree-based duration model for Standard Yorùbá text-to-speech synthesis
Computer Speech and Language
Predicting injection profiles using ANFIS
Information Sciences: an International Journal
Kernel shapes of fuzzy sets in fuzzy systems for function approximation
Information Sciences: an International Journal
Adaptive fuzzy priors for Bayesian inference
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Interface optimality in fuzzy inference systems
International Journal of Approximate Reasoning
Evolving takagi sugeno modelling with memory for slow processes
International Journal of Knowledge-based and Intelligent Engineering Systems
Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting
The Journal of Machine Learning Research
Standard additive fuzzy system for stock price forecasting
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Non-singleton genetic fuzzy logic system for arrhythmias classification
Engineering Applications of Artificial Intelligence
Flexible Takagi-Sugeno neuro-fuzzy structures
SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization
ACMOS'05 Proceedings of the 7th WSEAS international conference on Automatic control, modeling and simulation
A new scaling kernel-based fuzzy system with low computational complexity
CSR'06 Proceedings of the First international computer science conference on Theory and Applications
Fuzzy granulation-based cascade fuzzy neural networks optimized by GA-RSL
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
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The shape of if-part fuzzy sets affects how well feedforward fuzzy systems approximate continuous functions. We explore a wide range of candidate if-part sets and derive supervised learning laws that tune them. Then we test how well the resulting adaptive fuzzy systems approximate a battery of test functions. No one shape emerges as the best. The sine function often does well and has tractable learning, but its undulating side-lobes may have no linguistic meaning. This suggests that function-approximation accuracy may sometimes have to outweigh linguistic or philosophical interpretations. We divide the if-part sets into two large classes. The first consists of n-dimensional joint sets that factor into n scalar sets. These sets ignore the correlations among input vector components. Fuzzy systems suffer in general from exponential rule explosion in high dimensions when they blindly approximate functions. The factorable fuzzy sets themselves also suffer from a curse of dimensionality: they tend to become binary spikes in high dimension. The second class consists of the more general but less common n-dimensional joint sets that do not factor into n scalar fuzzy sets. We present a method for constructing such unfactorable joint sets from scalar distance measures. Fuzzy systems that use unfactorable sets need not suffer from exponential rule explosion but their increased complexity may lead to intractable learning and inscrutable if-then rules. We prove that some of these sets still suffer from spikiness