Introduction to artificial neural systems for pattern recognition
Computers & Operations ResearchPublished 1 January 1991
Laura I. Burke
Citations62
SJR quartileQ1
SJR score1.60
SNIP2.02
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The distinguishing features of neurally inspired systems are described, and popular systems in a discrete-time, algorithmic framework are presented in order to establish guidelines for their use in operations research.
Abstract
The artificial intelligence community has seen a recent resurgence in the area of neural network study. Inspired by the workings of the brain and nervous system, neural networks have solved some persistent problems in vision and speech processing. However, the new systems may offer an alternative approach to decision-making via high level pattern recognition. This paper will describe the distinguishing features of neurally inspired systems, and present popular systems in a discrete-time, algorithmic framework. Examples of applications to decision problems will appear, and guidelines for their use in operations research will be established.
Keywords
Computer ScienceEngineering
Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)Some methods for classification and analysis of multivariate observations
22,787 Citations1967James B. MacQueen
Pattern classification and scene analysis
12,643 Citations1973Richard O. Duda, Peter E. Hart
Psychological ReviewThe perceptron: A probabilistic model for information storage and organization in the brain.
11,746 Citations1958Frank F. Rosenblatt
This article will be concerned primarily with the second and third questions, which are still subject to a vast amount of speculation, and where the few relevant facts currently supplied by neurophysiology have not yet been integrated into an acceptable theory.
IEEE ASSP MagazineAn introduction to computing with neural nets
7,858 Citations1987Richard P. Lippmann
Biological Cybernetics“Neural” computation of decisions in optimization problems
6,040 Citations1985J. J. Hopfield, David W. Tank
Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.
Neural ComputationFast Learning in Networks of Locally-Tuned Processing Units
4,219 Citations1989John Moody, Christian J. Darken
This work proposes a network architecture which uses a single internal layer of locally-tuned processing units to learn both classification tasks and real-valued function approximations (Moody and Darken 1988).
Cognitive ScienceA Learning Algorithm for Boltzmann Machines*
3,324 Citations1985David H. Ackley, Geoffrey E. Hinton +1 more
Biological CyberneticsAdaptive pattern classification and universal recoding: I. Parallel development and coding of neural feature detectors
1,916 Citations1976Stephen Grossberg
It is shown how experience can retune feature detectors to respond to a prescribed convex set of spatial patterns, and a classification of adult feature detector properties in terms of a small number of functional principles is suggested.
Neural NetworksIncreased rates of convergence through learning rate adaptation
1,795 Citations1988Robert A. Jacobs
A study of Steepest Descent and an analysis of why it can be slow to converge and four heuristics for achieving faster rates of convergence are proposed.
Parallel Networks that Learn to Pronounce English Text
1,556 Citations1989Terrence J. Sejnowski
Cognitive ScienceCompetitive Learning: From Interactive Activation to Adaptive Resonance
1,476 Citations1987Stephen Grossberg
Functional and mechanistic comparisons are made between several network models of cognitive processing: competitive learning, interactive activation, adaptive resonance, and back propagation.
Applied OpticsART 2: self-organization of stable category recognition codes for analog input patterns
1,433 Citations1987Gail A. Carpenter, Stephen Grossberg
ART 2, a class of adaptive resonance architectures which rapidly self-organize pattern recognition categories in response to arbitrary sequences of either analog or binary input patterns, is introduced.
Neural NetworksAnalysis of hidden units in a layered network trained to classify sonar targets
1,111 Citations1988Rebecca Gorman, Terrence J. Sejnowski
A neural network learning procedure has been applied to the classification of sonar returns from two undersea targets, a metal cylinder and a similarly shaped rock, and network performance and classification strategy was comparable to that of trained human listeners.
Cognitive ScienceFeature Discovery by Competitive Learning*
1,073 Citations1985David E. Rumelhart, David Zipser
This paper shows how a set of feature detectors which capture important aspects of the set of stimulus input patterns are discovered and how these feature detectors form the basis of a multilayer system that serves to learn categorizations of stimulus sets which are not linearly separable.
Neural NetworksGeneralization of backpropagation with application to a recurrent gas market model
952 Citations1988Paul J. Werbos
This paper will derive a generalization of backpropagation to recurrent systems (which input their own output), such as hybrids of perceptron-style networks and Grossberg/Hopfield networks, and does not require the storage of intermediate iterations to deal with continuous recurrence.
Cognitive ScienceFeature discovery by competitive learning
890 Citations1985David E. Rumelhart, David Zipser
Journal of the American Statistical AssociationSome Asymptotic Results for Learning in Single Hidden-Layer Feedforward Network Models
436 Citations1989Halbert White
Neural NetworksSelf-organizing feature maps and the travelling salesman problem
341 Citations1988Bernard Angéniol, Gaël de La Croix Vaubois +1 more
Based on Kohonen's work on self-organizing feature maps, an algorithm for solving the classical Travelling Salesman Problem is derived, given a set of cities defined by their positions in the plane, which iteratively organizes towards a quasi-optimal solution.
ScholarlyCommons (University of Pennsylvania)Learning Algorithms for Connectionist Networks: Applied Gradient Methods of Nonlinear Optimization
239 Citations1988Raymond L. Watrous
It is shown that in plateau regions of relatively constant gradient, the momentum term acts to increase the step size by a factor of 1/1-μ, where μ is the momentumTerm, and in valley regions with steep sides,The momentum constant acts to focus the search direction toward the local minimum by averaging oscillations in the gradient.
MIT Press eBooksMaking a mind versus modeling the brain: aritifical intelligence back at a branchpoint
74 Citations1989H. L. Dreyfus, Stuart E. Dreyfus
Lecture notes in computer scienceBoltzmann machines and their applications
55 Citations1987Emile Aarts, Jan Korst
A formal model of the Boltzmann machine is presented and a discussion of two different applications of the model, viz. solving combinatorial optimization problems and carrying out learning tasks are discussed.