Journal ArticleDOI
Comparison of adaptive methods for function estimation from samples
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TLDR
A pragmatic framework for comparisons between various methods is described, and a detailed comparison study comprising several thousand individual experiments is presented, which provides some insights on applicability of various methods.Abstract:
The problem of estimating an unknown function from a finite number of noisy data points has fundamental importance for many applications. This problem has been studied in statistics, applied mathematics, engineering, artificial intelligence, and, more recently, in the fields of artificial neural networks, fuzzy systems, and genetic optimization. In spite of many papers describing individual methods, very little is known about the comparative predictive (generalization) performance of various methods. We discuss subjective and objective factors contributing to the difficult problem of meaningful comparisons. We also describe a pragmatic framework for comparisons between various methods, and present a detailed comparison study comprising several thousand individual experiments. Our approach to comparisons is biased toward general (nonexpert) users. Our study uses six representative methods described using a common taxonomy. Comparisons performed on artificial data sets provide some insights on applicability of various methods. No single method proved to be the best, since a method's performance depends significantly on the type of the target function, and on the properties of training data.read more
Citations
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Journal ArticleDOI
Deterministic job-shop scheduling: Past, present and future
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TL;DR: A subclass of the deterministic job-shop scheduling problem in which the objective is minimising makespan is sought, by providing an overview of the history, the techniques used and the researchers involved.
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Approximation of nonlinear systems with radial basis function neural networks
TL;DR: A technique for approximating a continuous function of n variables with a radial basis function (RBF) neural network is presented, which significantly reduces the network training and evaluation time and the resulting system is bounded-input bounded-output stable.
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Output Feedback NN Control for Two Classes of Discrete-Time Systems With Unknown Control Directions in a Unified Approach
TL;DR: In this paper, output feedback adaptive neural network (NN) controls are investigated for two classes of nonlinear discrete-time systems with unknown control directions: nonlinear pure-feedback systems and nonlinear autoregressive moving average with exogenous inputs (NARMAX).
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Trajectory Planning and Optimized Adaptive Control for a Class of Wheeled Inverted Pendulum Vehicle Models
Chenguang Yang,Zhijun Li,Jing Li +2 more
TL;DR: A neural-network-based adaptive generator of implicit control trajectory (AGICT) of the tilt angle which indirectly “controls” the forward velocity such that it tracks the desired velocity asymptotically is designed.
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Multiobjective evolutionary optimization of the size, shape, and position parameters of radial basis function networks for function approximation
Jesús González,Ignacio Rojas,Julio Ortega,Héctor Pomares,Francisco Javier Amores Fernández,Antonio F. Díaz +5 more
TL;DR: A multiobjective evolutionary algorithm to optimize radial basis function neural networks (RBFNNs) in order to approach target functions from a set of input-output pairs by including some new genetic operators in the evolutionary process.
References
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Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Journal ArticleDOI
Multivariate Adaptive Regression Splines
TL;DR: In this article, a new method is presented for flexible regression modeling of high dimensional data, which takes the form of an expansion in product spline basis functions, where the number of basis functions as well as the parameters associated with each one (product degree and knot locations) are automatically determined by the data.
Book
Pattern recognition and neural networks
Brian D. Ripley,N. L. Hjort +1 more
TL;DR: Professor Ripley brings together two crucial ideas in pattern recognition; statistical methods and machine learning via neural networks in this self-contained account.
Journal ArticleDOI
Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting
TL;DR: Locally weighted regression as discussed by the authors is a way of estimating a regression surface through a multivariate smoothing procedure, fitting a function of the independent variables locally and in a moving fashion analogous to how a moving average is computed for a time series.