Structure identification of fuzzy model
Fuzzy Sets and Systems
Probability density estimation and local basis function neural networks
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Neurofuzzy adaptive modelling and control
Neurofuzzy adaptive modelling and control
A Projection Pursuit Algorithm for Exploratory Data Analysis
IEEE Transactions on Computers
Fast learning in networks of locally-tuned processing units
Neural Computation
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This paper focuses on the use of kernel method and projection pursuit regression for non-parametric probability density estimation. Direct application of the kernel method is not able to pick up characteristic features of multidimensional density function. We propose a fuzzy projection pursuit density estimation based on the membership function and the eigenvector of the covariance matrix. Marginal densities along the subspace spanned by the projection vector are estimated. The proposed projection pursuit is one of the methods which are able to bypass the 'curse of dimensionality' in multidimensional density estimation. An application to experimental design for machining accuracy of end milling with the tool in small diameter is presented to demonstrate its usefulness.