By Stergios Stergiopoulos

Advances in electronic sign processing algorithms and computing device expertise have mixed to provide real-time platforms with features a ways past these of simply few years in the past. Nonlinear, adaptive equipment for sign processing have emerged to supply greater array achieve functionality, although, they lack the robustness of traditional algorithms. The problem continues to be to advance an idea that exploits some great benefits of both-a scheme that integrates those tools in useful, real-time systems.

The complex sign Processing instruction manual is helping you meet that problem. past providing an excellent creation to the foundations and purposes of complicated sign processing, it develops a commonplace processing constitution that takes good thing about the similarities that exist between radar, sonar, and clinical imaging structures and integrates traditional and nonlinear processing schemes

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**Extra info for Advanced signal processing handbook : theory and implementation for radar, sonar, and medical imaging real time systems**

**Sample text**

The sequential mode is best suited for pattern classification. 2. Batch mode: In this second mode of BP learning, adjustments are made to the free parameters of the network on an epoch-by-epoch basis, where each epoch consists of the entire set of training examples. The batch mode is best suited for nonlinear regression. The BP learning algorithm is simple to implement and computationally efficient in that its complexity is linear in the synaptic weights of the network. However, a major limitation of the algorithm is that it can be excruciatingly slow, particularly when we have to deal with a difficult learning task that requires the use of a large network.

For a detailed treatment of the subject, see Haykin (1999); this book has an up-to-date bibliography that occupies 41 pages of references. 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.

A filter is said to be linear if the filtered, smoothed, or predicted quantity at the output of the device is a linear function of the observations applied to the filter input. Otherwise, the filter is nonlinear. , mean and correlation functions) of the useful signal and unwanted additive noise, and the requirement is to design a linear filter with the noisy data as input so as to minimize the effects of noise at the filter output according to some statistical criterion. A useful approach to this filter-optimization problem is to minimize the mean-square value of the error signal that is defined as the difference between some desired response and the actual filter output.