This chapter is dedicated to the scope of which facts should be considered when deciding whether a Neural Network (NN) solution is suitable to solve a given problem. This is followed by a detailed example of a successful and useful application: a Neural Binary Detector
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M.S.Kim, C.C.Guest, Modification of backpropagation networks for complex-valued signal processing in frequency domain, IEEE Proc. Int. Conf. Neural Networks, IJCNN, Vol. III, pp. 27–31, San Diego, June 1990.
S. E. Decatur, Application of neural networks to terrain classification, Proc. Int. Conf. Neural Networks, pp. 283–288, 1989.
N. Miller, M.W. McKenna, T.C. Lau, Office of Naval Research Contributions to Neural Networks and Signal Processing in Oceanic Engineering, IEEE Journal of Oceanic Engineering, vol. 17, no. 4, Oct. 1992.
M.W. Roth, Survey of neural network technology for automatic target recognition, IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 28–43, Mar. 1990.
D.W. Ruck, S.K. Rogers, M. Kabrisky, M.E. Oxley, B.W. Suter, The Multilayer Perceptron as an Approximation to a Bayes Optimal Discriminant Function, IEEE Trans. on Neural Networks, vol. 1, no. 4, pp. 296–298, Dec. 1990.
H.L. Van Trees, Detection, Estimation and Modulation Theory, Part I, Eds. Wiley and Sons, New York, 1968.
D.R. Hush and B.G. Horne, Progress in Supervised Neural Networks. What’s new since Lippmann?, IEEE Signal Processing Magazine, pp. 8–51, Jan. 1993.
B.A. Telfer, H.H. Szu, Implementing the Minimum-Misclassification-Error Energy Function for Target Recognition, Neural Networks, vol. 7, no. 5, pp. 809–818, 1994.
B.A. Telfer, H.H. Szu, Energy Functions for Minimizing Misclassification Error With Minimum-Complexity Networks, Proc. of Int. Joint Conf. Neural Networks, IJCNN, vol IV, pp. 214–219, 1992.
A. El-Jaroudi, J. Makhoul, A New Error Criterion For Posterior Probability Estimation With Neural Nets, Proc. of Int. Joint. Conf. Neural Networks, IJCNN, vol. I, no. 5, pp. 185–192, 1990.
D. Andina, J.L. Sanz-Gonzàlez, On the problem of Binary Detection with Neural Networks, Proc. of 38 Midwest Symposium on Circuits and Systems, Rio de Janeiro, Brazil, vol. I, pp. 554–557, Aug. 1995.
W.L. Root, An Introduction to the Theory of the Detection of Signals in Noise, Proc. of the IEEE, vol 58, pp. 610–622, May 1970.
D.Andina, Optimizaciòn de Detectores Neuronales: Aplicaciòn a Radar y Sonar, Ph. D. Dissertation (in Spanish), ETSIT-M, Polytechnic University of Madrid, Spain, Dec. 1995.
J.L. Marcum, A Statistical Theory of Target Detection by Pulsed Radar, IRE Trans. on Information Theory, vol. IT-6, no. 2, pp. 59–144. Apr. 1960.
E. Barnard, D. Casasent, A Comparison Between Criterion Functions for Linear Classifiers, with Application to Neural Nets, IEEE Trans. Systems, Man, and Cybernetics, vol. 19, no. 5, pp. 1030–1040, Oct. 1989.
P. Swerling, Probability of detection for fluctuating targets, IRE Trans. on Information Theory, vol. IT-6, no. 2, pp. 269–308, Apr. 1960.
D. Andina, J.L. Sanz-Gonzàlez, J.A. Jimènez-Pajares, A Comparison of Criterion Functions for a Neural Network Applied to Binary Detection, Proc. of Int. Conf. Neural Networks, ICNN, Perth, Australia, Vol I, pp. 329–333, Nov. 1995.
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Andina, D., Vega-Corona, A., Seijas, J.I., Alarcòn, M.J. (2007). Application of Neural Networks. In: Andina, D., Pham, D.T. (eds) Computational Intelligence. Springer, Boston, MA. https://doi.org/10.1007/0-387-37452-3_4
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