By Gérard Dreyfus

Neural networks symbolize a strong information processing method that has reached adulthood and large software. whilst truly understood and thoroughly used, they're a needed part within the toolbox of any engineer who wishes make the simplest use of the to be had information, to be able to construct versions, make predictions, mine info, realize shapes or indications, and so forth. starting from theoretical foundations to real-life purposes, this e-book is meant to supply engineers and researchers with transparent methodologies for benefiting from neural networks in commercial, monetary or banking purposes, many circumstances of that are awarded within the ebook. For the good thing about readers wishing to achieve deeper wisdom of the subjects, the booklet positive aspects appendices that supply theoretical information for higher perception, and algorithmic info for effective programming and implementation. The chapters were written via specialists ands seemlessly edited to offer a coherent and accomplished, but no longer redundant, practcally-oriented introduction.

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An overview of the tasks implied in nonlinear model design was presented, together with conditions for successful applications. In Chap. 2, the reader will find all necessary details for neural network training, for input selection and for model selection, both for static models (feedforward neural networks) and for dynamic model (recurrent neural networks). 32 G. Dreyfus Fig. 15. Training of a feedforward neural network with one input variable and three hidden neurons. The line is the output of the model, the crosses are the elements of the training set.

It should be absolutely clear that the generalization ability is necessarily limited: it cannot extend beyond the boundaries of the region of input space where training examples are present, as shown on Fig. 9. The estimation of the generalization ability is an important question that will be examined in detail in the present book. Fig. 12. An approximation of the regression function, performed by a neural network, from the experimental points of Fig. 11 20 G. 2 To What Extent Is Parsimony a Valuable Property?

Feedforward neural networks are more and more frequently used for data mining applications. Chapter 6 of the present book is fully devoted to feedforward neural networks and support vector machines for discrimination. 5 Feedforward Neural Networks with Unsupervised Training for Data Analysis and Visualization Due to the development of powerful data processing and storage systems, very large amounts of information are available, whether in the form of numbers (intensive data processing of experimental results) or in the form of symbols (text corpuses).

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