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Regression Modeling in BackPropagation and Projection Pursuit Learning
, 1994
"... We studied and compared two types of connectionist learning methods for modelfree regression problems in this paper. One is the popular backpropagation learning (BPL) well known in the artificial neural networks literature; the other is the projection pursuit learning (PPL) emerged in recent years ..."
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Cited by 66 (1 self)
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We studied and compared two types of connectionist learning methods for modelfree regression problems in this paper. One is the popular backpropagation learning (BPL) well known in the artificial neural networks literature; the other is the projection pursuit learning (PPL) emerged in recent years in the statistical estimation literature. Both the BPL and the PPL are based on projections of the data in directions determined from interconnection weights. However, unlike the use of fixed nonlinear activations (usually sigmoidal) for the hidden neurons in BPL, the PPL systematically approximates the unknown nonlinear activations. Moreover, the BPL estimates all the weights simultaneously at each iteration, while the PPL estimates the weights cyclically (neuronbyneuron and layerbylayer) at each iteration. Although the BPL and the PPL have comparable training speed when based on a GaussNewton optimization algorithm, the PPL proves more parsimonious in that the PPL requires a fewer hi...
Ridge polynomial networks
 IEEE Transactions on Neural Networks
, 1995
"... AbstructThis paper presents P polynomial conndo&t network called ridge polynomial network (RE”) that can dormly approximate any imntinuous function on a cootpad set in multidimensional input space?TId, with arbitrary dqpe of pccmcy. Thii network provides a more e$cicnt and regular orchitccture comp ..."
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Cited by 20 (3 self)
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AbstructThis paper presents P polynomial conndo&t network called ridge polynomial network (RE”) that can dormly approximate any imntinuous function on a cootpad set in multidimensional input space?TId, with arbitrary dqpe of pccmcy. Thii network provides a more e$cicnt and regular orchitccture compared to ordinary higherorder feedforward networks while maintaining their fast learning property. The ridge polynomial network is a generalization of the pisigma network and uses a special form of ridge polynomials. It function f: Bd + B is approximated as [17], [25] / d d d d d d provides a natural mechanism for irmmental ntbtwnk growth. Simulation results on a surface fitting problem, the dassiecPtion of highdimensional data and the realbtion of a mdtlvariate polynomial function are given to highlight the network. In particular, a canstructive 1 developed for the network is shown to yield smooth generalization and steady learning. I.
Human Control Strategy: Abstraction, Verification, and Replication
 IEEE Control Systems Magazine
, 1997
"... this article, we describe and develop methodologies for mod eling and transferring human control strategy (HCS). This research has potential application in a variety of areas such as the Intelligent Vehicle Highway System (IVHS), humanmachine interfacing, realtime training, space telerobotics, an ..."
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Cited by 17 (6 self)
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this article, we describe and develop methodologies for mod eling and transferring human control strategy (HCS). This research has potential application in a variety of areas such as the Intelligent Vehicle Highway System (IVHS), humanmachine interfacing, realtime training, space telerobotics, and agile manufacturing. We specifically address the following issues: (1) how to efficiently model human control strategy through learning cascade neural networks, (2) how to select state inputs in order to generate reliable models, (3) how to validate the computed models through an independent, Hidden Markov Modelbased procedure, and (4) how to effectively transfer human control strategy. We have implemented this approach experimentally in the realtime control of a human driving simulator, and are working to transfer these methodologies for the control of an autonomous vehicle and a mobile robot. In providing a framework for abstracting computational models of human skill, we expect to facilitate analysis of human control, the development of humanlike intelligent machines, improved humanrobot coordination, and the transfer of skill from one human to another
Implementing Projection Pursuit Learning
, 1996
"... This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations ..."
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Cited by 11 (0 self)
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This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a one hidden layer sigmoidal neural network shows why grouping hidden units in a projection pursuit learning network is useful. Learning robot arm inverse dynamics is used as an example problem.
Use of Bias Term in Projection Pursuit Learning Improves Approximation and Convergence Properties
 IEEE Trans. Neural Networks
, 1996
"... In a regression problem, one is given a d dimensional random vector X, the components of which are called predictor variables, and a random variable, Y , called response. A regression surface describes a general relationship between variables X and Y . One nonparametric regression technique that h ..."
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Cited by 6 (1 self)
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In a regression problem, one is given a d dimensional random vector X, the components of which are called predictor variables, and a random variable, Y , called response. A regression surface describes a general relationship between variables X and Y . One nonparametric regression technique that has been successfully applied to highdimensional data is projection pursuit regression (PPR). In this method, the regression surface is approximated by a sum of empirically determined univariate functions of linear combinations of the predictors. Projection pursuit learning (PPL) proposed by Hwang et al. formulates PPR using a twolayer feedforward neural network. One of the main differences between PPR and PPL is that the smoothers in PPR are nonparametric, whereas those in PPL are based on Hermite functions of some predefined highest order R. While the convergence property of PPR is already known, that for PPL has not been thoroughly studied. In this paper, we demonstrate that PPL networks...