ANFIS: adaptive-network-based fuzzy inference system
JSR Jang - IEEE transactions on systems, man, and …, 1993 - ieeexplore.ieee.org
The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy
inference system) is presented, which is a fuzzy inference system implemented in the …
inference system) is presented, which is a fuzzy inference system implemented in the …
A function estimation approach to sequential learning with neural networks
V Kadirkamanathan, M Niranjan - Neural computation, 1993 - direct.mit.edu
In this paper, we investigate the problem of optimal sequential learning, viewed as a
problem of estimating an underlying function sequentially rather than estimating a set of …
problem of estimating an underlying function sequentially rather than estimating a set of …
[图书][B] Handbook of neural computation
E Fiesler, R Beale - 2020 - books.google.com
The Handbook of Neural Computation is a practical, hands-on guide to the design and
implementation of neural networks used by scientists and engineers to tackle difficult and/or …
implementation of neural networks used by scientists and engineers to tackle difficult and/or …
Time series forecasting by combining RBF networks, certainty factors, and the Box-Jenkins model
DK Wedding II, KJ Cios - Neurocomputing, 1996 - Elsevier
A method is described for using Radial Basis Function (RBF) neural networks to generate
certainty factors along with normal output. When RBF output with low certainty factors values …
certainty factors along with normal output. When RBF output with low certainty factors values …
On the persistency of excitation in radial basis function network identification of nonlinear systems
D Gorinevsky - IEEE Transactions on Neural Networks, 1995 - ieeexplore.ieee.org
Considers radial basis function (RBF) network approximation of a multivariate nonlinear
mapping as a linear parametric regression problem. Linear recursive identification …
mapping as a linear parametric regression problem. Linear recursive identification …
Fsfrt: Forecasting system for red tides. a hybrid autonomous ai model
F Fdez-Riverola, JM Corchado - Applied Artificial Intelligence, 2003 - Taylor & Francis
A hybrid neuro-symbolic problem-solving model is presented in which the aim is to forecast
parameters of a complex and dynamic environment in an unsupervised way. In situations in …
parameters of a complex and dynamic environment in an unsupervised way. In situations in …
On radial basis function nets and kernel regression: Statistical consistency, convergence rates, and receptive field size
Useful connections between radial basis function (RBF) nets and kernel regression
estimators (KRE) are established. By using existing theoretical results obtained for KRE as …
estimators (KRE) are established. By using existing theoretical results obtained for KRE as …
Quantitative structure− property relationships for the estimation of boiling point and flash point using a radial basis function neural network
J Tetteh, T Suzuki, E Metcalfe… - Journal of chemical …, 1999 - ACS Publications
Radial basis function (RBF) neural network models for the simultaneous estimation of flash
point (T f) and boiling point (T b) based on 25 molecular functional groups and their first …
point (T f) and boiling point (T b) based on 25 molecular functional groups and their first …
A neural-network learning theory and a polynomial time RBF algorithm
A Roy, S Govil, R Miranda - IEEE Transactions on Neural …, 1997 - ieeexplore.ieee.org
This paper presents a new learning theory (a set of principles for brain-like learning) and a
corresponding algorithm for the neural-network field. The learning theory defines …
corresponding algorithm for the neural-network field. The learning theory defines …
Some extensions of radial basis functions and their applications in artificial intelligence
F Girosi - Computers & Mathematics with Applications, 1992 - Elsevier
Abstract Radial Basis Functions have recently found interesting applications in Artificial
Intelligence, and in particular in the problem of learning to perform a particular task from a …
Intelligence, and in particular in the problem of learning to perform a particular task from a …