Results 1  10
of
64
AdaptiveNetworkBased Fuzzy Inference System
 IEEE Transactions on Systems, Man, and Cybernetics
, 1993
"... ..."
Neurofuzzy modeling and control
 IEEE Proceedings
, 1995
"... Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framew ..."
Abstract

Cited by 152 (1 self)
 Add to MetaCart
Abstract  Fundamental and advanced developments in neurofuzzy synergisms for modeling and control are reviewed. The essential part of neurofuzzy synergisms comes from a common framework called adaptive networks, which uni es both neural networks and fuzzy models. The fuzzy models under the framework of adaptive networks is called ANFIS (AdaptiveNetworkbased Fuzzy Inference System), which possess certain advantages over neural networks. We introduce the design methods for ANFIS in both modeling and control applications. Current problems and future directions for neurofuzzy approaches are also addressed. KeywordsFuzzy logic, neural networks, fuzzy modeling, neurofuzzy modeling, neurofuzzy control, ANFIS. I.
The Unscented Kalman Filter for nonlinear estimation
, 2000
"... The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a ne ..."
Abstract

Cited by 74 (4 self)
 Add to MetaCart
The Extended Kalman Filter (EKF) has become a standard technique used in a number of nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system, estimating parameters for nonlinear system identification (e.g., learning the weights of a neural network), and dual estimation (e.g., the Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. This paper points out the flaws in using the EKF, and introduces an improvement, the Unscented Kalman Filter (UKF), proposed by Julier and Uhlman [5]. A central and vital operation performed in the Kalman Filter is the propagation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is approximated
Local Gain Adaptation in Stochastic Gradient Descent
 In Proc. Intl. Conf. Artificial Neural Networks
, 1999
"... Gain adaptation algorithms for neural networks typically adjust learning rates by monitoring the correlation between successive gradients. Here we discuss the limitations of this approach, and develop an alternative by extending Sutton's work on linear systems to the general, nonlinear case. Th ..."
Abstract

Cited by 59 (12 self)
 Add to MetaCart
Gain adaptation algorithms for neural networks typically adjust learning rates by monitoring the correlation between successive gradients. Here we discuss the limitations of this approach, and develop an alternative by extending Sutton's work on linear systems to the general, nonlinear case. The resulting online algorithms are computationally little more expensive than other acceleration techniques, do not assume statistical independence between successive training patterns, and do not require an arbitrary smoothing parameter. In our benchmark experiments, they consistently outperform other acceleration methods, and show remarkable robustness when faced with noni. i.d. sampling of the input space.
SigmaPoint Kalman Filters for Probabilistic Inference in Dynamic StateSpace Models
 In Proceedings of the Workshop on Advances in Machine Learning
, 2003
"... Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the post ..."
Abstract

Cited by 50 (5 self)
 Add to MetaCart
Probabilistic inference is the problem of estimating the hidden states of a system in an optimal and consistent fashion given a set of noisy or incomplete observations. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive online.
Fast Curvature MatrixVector Products for SecondOrder Gradient Descent
 Neural Computation
, 2002
"... We propose a generic method for iteratively approximating various secondorder gradient steps  Newton, GaussNewton, LevenbergMarquardt, and natural gradient  in linear time per iteration, using special curvature matrixvector products that can be computed in O(n). Two recent acceleration techn ..."
Abstract

Cited by 40 (14 self)
 Add to MetaCart
We propose a generic method for iteratively approximating various secondorder gradient steps  Newton, GaussNewton, LevenbergMarquardt, and natural gradient  in linear time per iteration, using special curvature matrixvector products that can be computed in O(n). Two recent acceleration techniques for online learning, matrix momentum and stochastic metadescent (SMD), in fact implement this approach. Since both were originally derived by very different routes, this o ers fresh insight into their operation, resulting in further improvements to SMD.
Sequential Monte Carlo Methods to Train Neural Network Models
, 2000
"... We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/ sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequentia ..."
Abstract

Cited by 34 (8 self)
 Add to MetaCart
We discuss a novel strategy for training neural networks using sequential Monte Carlo algorithms and propose a new hybrid gradient descent/ sampling importance resampling algorithm (HySIR). In terms of computational time and accuracy, the hybrid SIR is a clear improvement over conventional sequential Monte Carlo techniques. The new algorithm may be viewed as a global optimization strategy that allows us to learn the probability distributions of the network weights and outputs in a sequential framework. It is well suited to applications involving online, nonlinear, and nongaussian signal processing. We show how the new algorithm outperforms extended Kalman filter training on several problems. In particular, we address the problem of pricing option contracts, traded in financial markets. In this context, we are able to estimate the onestepahead probability density functions of the options prices.
OnLine Learning Processes in Artificial Neural Networks
, 1993
"... We study online learning processes in artificial neural networks from a general point of view. Online learning means that a learning step takes place at each presentation of a randomly drawn training pattern. It can be viewed as a stochastic process governed by a continuoustime master equation. O ..."
Abstract

Cited by 32 (4 self)
 Add to MetaCart
We study online learning processes in artificial neural networks from a general point of view. Online learning means that a learning step takes place at each presentation of a randomly drawn training pattern. It can be viewed as a stochastic process governed by a continuoustime master equation. Online learning is necessary if not all training patterns are available all the time. This occurs in many applications when the training patterns are drawn from a timedependent environmental distribution. Studying learning in a changing environment, we encounter a conflict between the adaptability and the confidence of the network's representation. Minimization of a criterion incorporating both effects yields an algorithm for online adaptation of the learning parameter. The inherent noise of online learning makes it possible to escape from undesired local minima of the error potential on which the learning rule performs (stochastic) gradient descent. We try to quantify these often made cl...
Feature Selection Using a Multilayer Perceptron
 Neural Network Comput
, 1990
"... The problem of selecting the best set of features for target recognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant ..."
Abstract

Cited by 26 (1 self)
 Add to MetaCart
The problem of selecting the best set of features for target recognition using a multilayer perceptron is addressed in this paper. A technique has been developed which analyzes the weights in a multilayer perceptron to determine which features the network finds important and which are unimportant. A brief introduction to the use of multilayer perceptrons for classification and the training rules available is followed by the mathematical development of the saliency measure for multilayer perceptrons. The technique is applied to two different image databases and is found to be consistent with statistical techniques and independent of the network initial conditions. The saliency measure is then used to compare the results of two different training rules on a target recognition problem. 1 Introduction Recently there has been a great deal of interest in the use of multilayer perceptrons as classifiers in pattern recognition problems (see, for example, [11]). Unfortunately, little ...