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[Beal72] Beale, E.M.L., “A derivation of conjugate gradients,” in F.A. Lootsma, Ed., Numerical methods for nonlinear optimization, London: Academic Press, 1972.
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[Caud89] Caudill, M., Neural Networks Primer, San Francisco, CA: Miller Freeman Publications, 1989.
This collection of papers from the AI Expert Magazine gives an excellent introduction to the field of neural networks. The papers use a minimum of mathematics to explain the main results clearly. Several good suggestions for further reading are included.
[CaBu92] Caudill, M., and C. Butler, Understanding Neural Networks: Computer Explorations, Vols. 1 and 2, Cambridge, MA: The MIT Press, 1992.
This is a two-volume workbook designed to give students “hands on” experience with neural networks. It is written for a laboratory course at the senior or first-year graduate level. Software for IBM PC and Apple Macintosh computers is included. The material is well written, clear, and helpful in understanding a field that traditionally has been buried in mathematics.
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This paper gives an excellent introduction to the field of radial basis functions. The papers use a minimum of mathematics to explain the main results clearly. Several good suggestions for further reading are included.
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This book is a compendium of knowledge of neural networks as they were known to 1988. It presents the theoretical foundations of neural networks and discusses their current applications. It contains sections on associative memories, recurrent networks, vision, speech recognition, and robotics. Finally, it discusses simulation tools and implementation technology.
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[DeHa01b] De Jesús, O., and M.T. Hagan, “Forward Perturbation Algorithm for a General Class of Recurrent Network,” Proceedings of the International Joint Conference on Neural Networks, Washington, DC, July 15–19, 2001, pp. 2626–2631.
[DeHa07] De Jesús, O., and M.T. Hagan, “Backpropagation Algorithms for a Broad Class of Dynamic Networks,” IEEE Transactions on Neural Networks, Vol. 18, No. 1, January 2007, pp. 14 -27.
This paper provides detailed algorithms for the calculation of gradients and Jacobians for arbitrarily-connected neural networks. Both the backpropagation-through-time and real-time recurrent learning algorithms are covered.
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[Elma90] Elman, J.L., “Finding structure in time,” Cognitive Science, Vol. 14, 1990, pp. 179–211.
This paper is a superb introduction to the Elman networks described in Chapter 10, “Recurrent Networks.”
[FeTs03] Feng, J., C.K. Tse, and F.C.M. Lau, “A neural-network-based channel-equalization strategy for chaos-based communication systems,” IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, Vol. 50, No. 7, 2003, pp. 954–957.
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[FoHa97] Foresee, F.D., and M.T. Hagan, “Gauss-Newton approximation to Bayesian regularization,” Proceedings of the 1997 International Joint Conference on Neural Networks, 1997, pp. 1930–1935.
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This book contains articles summarizing Grossberg's theoretical psychophysiology work up to 1980. Each article contains a preface explaining the main points.
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This paper reports the first development of the Levenberg-Marquardt algorithm for neural networks. It describes the theory and application of the algorithm, which trains neural networks at a rate 10 to 100 times faster than the usual gradient descent backpropagation method.
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This data set was taken from the StatLib library, which is maintained at Carnegie Mellon University.
[HDB96] Hagan, M.T., H.B. Demuth, and M.H. Beale, Neural Network Design, Boston, MA: PWS Publishing, 1996.
This book provides a clear and detailed survey of basic neural network architectures and learning rules. It emphasizes mathematical analysis of networks, methods of training networks, and application of networks to practical engineering problems. It has example programs, an instructor’s guide, and transparency overheads for teaching.
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This paper describes spurious valleys that appear in the error surfaces of recurrent networks. It also explains how training algorithms can be modified to avoid becoming stuck in these valleys.
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This book proposed neural network architectures and the first learning rule. The learning rule is used to form a theory of how collections of cells might form a concept.
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[Joll86] Jolliffe, I.T., Principal Component Analysis, New York: Springer-Verlag, 1986.
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This book analyzes several learning rules. The Kohonen learning rule is then introduced and embedded in self-organizing feature maps. Associative networks are also studied.
[Koho97] Kohonen, T., Self-Organizing Maps, Second Edition, Berlin: Springer-Verlag, 1997.
This book discusses the history, fundamentals, theory, applications, and hardware of self-organizing maps. It also includes a comprehensive literature survey.
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This paper discusses a class of neural networks described by first-order linear differential equations that are defined on a closed hypercube. The systems considered retain the basic structure of the Hopfield model but are easier to analyze and implement. The paper presents an efficient method for determining the set of asymptotically stable equilibrium points and the set of unstable equilibrium points. Examples are presented. The method of Li, et. al., is implemented in Advanced Topics in the User’s Guide.
[Lipp87] Lippman, R.P., “An introduction to computing with neural nets,” IEEE ASSP Magazine, 1987, pp. 4–22.
This paper gives an introduction to the field of neural nets by reviewing six neural net models that can be used for pattern classification. The paper shows how existing classification and clustering algorithms can be performed using simple components that are like neurons. This is a highly readable paper.
[MacK92] MacKay, D.J.C., “Bayesian interpolation,” Neural Computation, Vol. 4, No. 3, 1992, pp. 415–447.
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A classic paper that describes a model of a neuron that is binary and has a fixed threshold. A network of such neurons can perform logical operations.
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This paper describes a two-layer network that first learned the truck dynamics and then learned how to back the truck to a specified position at a loading dock. To do this, the neural network had to solve a highly nonlinear control systems problem.
[NgWi90] Nguyen, D., and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” Proceedings of the International Joint Conference on Neural Networks, Vol. 3, 1990, pp. 21–26.
Nguyen and Widrow show that a two-layer sigmoid/linear network can be viewed as performing a piecewise linear approximation of any learned function. It is shown that weights and biases generated with certain constraints result in an initial network better able to form a function approximation of an arbitrary function. Use of the Nguyen-Widrow (instead of purely random) initial conditions often shortens training time by more than an order of magnitude.
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This book presents all of Rosenblatt's results on perceptrons. In particular, it presents his most important result, the perceptron learning theorem.
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This is a basic reference on backpropagation.
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These two volumes contain a set of monographs that present a technical introduction to the field of neural networks. Each section is written by different authors. These works present a summary of most of the research in neural networks to the date of publication.
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Backpropagation learning can be speeded up and made less sensitive to small features in the error surface such as shallow local minima by combining techniques such as batching, adaptive learning rate, and momentum.
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This is a basic paper on adaptive signal processing.