The Roots of Backpropagation

Informationen zum Autor PAUL JOHN WERBOS is a Program Director in the Engineering Directorate of the National Science Foundation as well as Past President of the International Neural Network Society. Previously, he developed and evaluated large-scale forecasting models at the Energy Information Administration of the Department of Energy, using backpropagation and other techniques discussed in this book. He has contributed, as a writer or editor, to several books on neural networks and has published more than forty journal articles and conference papers on a wide range of subjects. Klappentext Now, for the first time, publication of the landmark work in backpropagation! Scientists, engineers, statisticians, operations researchers, and other investigators involved in neural networks have long sought direct access to Paul Werbos's groundbreaking, much-cited 1974 Harvard doctoral thesis, The Roots of Backpropagation, which laid the foundation of backpropagation. Now, with the publication of its full text, these practitioners can go straight to the original material and gain a deeper, practical understanding of this unique mathematical approach to social studies and related fields. In addition, Werbos has provided three more recent research papers, which were inspired by his original work, and a new guide to the field. Originally written for readers who lacked any knowledge of neural nets, The Roots of Backpropagation firmly established both its historical and continuing significance as it: Demonstrates the ongoing value and new potential of backpropagation Creates a wealth of sound mathematical tools useful across disciplines Sets the stage for the emerging area of fast automatic differentiation Describes new designs for forecasting and control which exploit backpropagation Unifies concepts from Freud, Jung, biologists, and others into a new mathematical picture of the human mind and how it works Certifies the viability of Deutsch's model of nationalism as a predictive tool--as well as the utility of extensions of this central paradigm "What a delight it was to see Paul Werbos rediscover Freud's version of 'back-propagation.' Freud was adamant (in The Project for a Scientific Psychology) that selective learning could only take place if the presynaptic neuron was as influenced as is the postsynaptic neuron during excitation. Such activation of both sides of the contact barrier (Freud's name for the synapse) was accomplished by reducing synaptic resistance by the absorption of 'energy' at the synaptic membranes. Not bad for 1895! But Werbos 1993 is even better." --Karl H. Pribram Professor Emeritus, Stanford University Zusammenfassung An introduction to the concept of backpropagation! or "dynamic feedback"! an algorithm developed by the author to be used in neural networking. The text initiates a body of background tools capable of generating applications across a range of fields! from engineering to political forecasting. Inhaltsverzeichnis THESIS. Beyond Regression: New Tools for Prediction and Analysis in theBehavioral Sciences. Dynamic Feedback, Statistical Estimation, and Systems Optimization:General Techniques. The Multivariate ARMA(1,1) Model: Its Significance andEstimation. Simulation Studies of Techniques of Time-Series Analysis. General Applications of These Ideas: Practical Hazards and NewPossibilities. Nationalism and Social Communications: A Test Case for MathematicalApproaches. APPLICATIONS AND EXTENSIONS. Forms of Backpropagation for Sensitivity Analysis, Optimization,and Neural Networks. Backpropagation Through Time: What It Does and How to Do It. Neurocontrol: Where It Is Going and Why It Is Crucial. Neural Networks and the Human Mind: New Mathematics Fits HumanisticInsight. Index....

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Now, for the first time, publication of the landmark work in backpropagation! Scientists, engineers, statisticians, operations researchers, and other investigators involved in neural networks have long sought direct access to Paul Werbos's groundbreaking, much-cited 1974 Harvard doctoral thesis, The Roots of Backpropagation, which laid the foundation of backpropagation. Now, with the publication of its full text, these practitioners can go straight to the original material and gain a deeper, practical understanding of this unique mathematical approach to social studies and related fields. In addition, Werbos has provided three more recent research papers, which were inspired by his original work, and a new guide to the field. Originally written for readers who lacked any knowledge of neural nets, The Roots of Backpropagation firmly established both its historical and continuing significance as it:

• Demonstrates the ongoing value and new potential of backpropagation
• Creates a wealth of sound mathematical tools useful across disciplines
• Sets the stage for the emerging area of fast automatic differentiation
• Describes new designs for forecasting and control which exploit backpropagation
• Unifies concepts from Freud, Jung, biologists, and others into a new mathematical picture of the human mind and how it works
• Certifies the viability of Deutsch's model of nationalism as a predictive tool--as well as the utility of extensions of this central paradigm "What a delight it was to see Paul Werbos rediscover Freud's version of 'back-propagation.' Freud was adamant (in The Project for a Scientific Psychology) that selective learning could only take place if the presynaptic neuron was as influenced as is the postsynaptic neuron during excitation. Such activation of both sides of the contact barrier (Freud's name for the synapse) was accomplished by reducing synaptic resistance by the absorption of 'energy' at the synaptic membranes. Not bad for 1895! But Werbos 1993 is even better." --Karl H. Pribram Professor Emeritus, Stanford University

Résumé
An introduction to the concept of backpropagation, or "dynamic feedback", an algorithm developed by the author to be used in neural networking. The text initiates a body of background tools capable of generating applications across a range of fields, from engineering to political forecasting.

Contenu

THESIS.

Beyond Regression: New Tools for Prediction and Analysis in theBehavioral Sciences.

Dynamic Feedback, Statistical Estimation, and Systems Optimization:General Techniques.

The Multivariate ARMA(1,1) Model: Its Significance andEstimation.

Simulation Studies of Techniques of Time-Series Analysis.

General Applications of These Ideas: Practical Hazards and NewPossibilities.

Nationalism and Social Communications: A Test Case for MathematicalApproaches.

APPLICATIONS AND EXTENSIONS.

Forms of Backpropagation for Sensitivity Analysis, Optimization,and Neural Networks.

Backpropagation Through Time: What It Does and How to Do It.

Neurocontrol: Where It Is Going and Why It Is Crucial.

Neural Networks and the Human Mind: New Mathematics Fits HumanisticInsight.

Index.