Advanced signal processing handbook : theory and by Stergios Stergiopoulos

By Stergios Stergiopoulos

Advances in electronic sign processing algorithms and machine know-how have mixed to provide real-time platforms with functions some distance past these of simply few years in the past. Nonlinear, adaptive tools for sign processing have emerged to supply greater array achieve functionality, even if, they lack the robustness of traditional algorithms. The problem is still to increase an idea that exploits the benefits of both-a scheme that integrates those tools in functional, real-time systems.

The complex sign Processing instruction manual is helping you meet that problem. past delivering a superb creation to the foundations and functions of complex sign processing, it develops a widely used processing constitution that takes good thing about the similarities that exist between radar, sonar, and clinical imaging platforms and integrates traditional and nonlinear processing schemes

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7. 8. 11) From these definitions, we readily see that the logistic function is of a unipolar form that is nonsymmetric, whereas the hyperbolic function is bipolar that is antisymmetric. 6 Layer of hidden neurons Fully connected feedforward of acyclic network with one hidden layer and one output layer. 7 Layer of output neurons Nonlinear unit Simplified model of a neuron. 6 layer by layer. The forward phase finishes with the computation of an error signal defined as the difference between a desired response and the actual output produced by the network in response to the signals applied to the input layer.

A. E. P. M. Mueller, The NMR phased array, Magn. Reson. , 16, 192–225, 1990. 19. S. A. V. Mulkern, Partial RF echo planar imaging with the FAISE method. I. Experimental and theoretical assessment of artifact, Magn. Reson. , 26, 328–341, 1992. 20. L. Owsley, Sonar Array Processing, S. V. , p. 123, Prentice-Hall, Englewood Cliffs, NJ, 1985. 21. B. Van Veen and K. , 4–24, 1988. 22. H. Sayed and T. , July, 18–60, 1994. 23. J. M. Carey, and S. Stergiopoulos, Editorial special issue on acoustic synthetic aperture processing, IEEE J.

In the context of adaptive signal-processing applications, neural networks offer the following advantages: • Nonlinearity, which makes it possible to account for the nonlinear behavior of physical phenomena responsible for generating the input data • The ability to approximate any prescribed input-output mapping of a continuous nature • Weak statistical assumptions about the environment, in which the network is embedded • Learning capability, which is accomplished by undertaking a training session with input-output examples that are representative of the environment • Generalization, which refers to the ability of the neural network to provide a satisfactory performance in response to test data never seen by the network before • Fault tolerance, which means that the network continues to provide an acceptable performance despite the failure of some neurons in the network • VLSI implementability, which exploits the massive parallelism built into the design of a neural network ©2001 CRC Press LLC This is indeed an impressive list of attributes, which accounts for the widespread interest in the use of neural networks to solve signal-processing tasks that are too difficult for conventional (linear) adaptive filters.

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