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The filtering of real world signals requires an adaptive mode of operation to deal with the statistically nonstationary nature of the data. Feedback and nonlinearity within filtering architectures are needed to cater for long time dependencies and possibly nonlinear signal generating mechanisms.

New technologies in engineering, physics and biomedicine are demanding increasingly complex methods of digital signal processing. By presenting the latest research work the authors demonstrate how real-time recurrent neural networks (RNNs) can be implemented to expand the range of traditional signal processing techniques and to help combat the problem of prediction. Within this text neural networks are considered as massively interconnected nonlinear adaptive filters.

• Analyses the relationships between RNNs and various nonlinear models and filters, and introduces spatio-temporal...

What shapes the cultural, political and ideological values of young people living in Southeastern Europe? Which identities matter to them? How are their values changing, and how can they be changed? Who is changing them? Europe's periphery is the testing ground for the success of European values and identities. The future stability and political coherence of the Union will be determined in large measure by identity issues in this region.

This book examines the ways in which ethnic and national values and identities have been surpassed as the overriding focus in the...

This book brings together the latest research achievements from signal processing and related disciplines, consolidating existing and proposed directions in DSP-based knowledge extraction and information fusion. The book includes contributions presenting both novel algorithms and existing applications, emphasizing on-line processing of real-world data. Readers discover applications that solve biomedical, industrial, and environmental problems.