Guest Editors' Introduction: Neural Networks For Signal Processing

P ROGRESS in the theory and design of neural networks has expanded on many fronts during the past ten years. Much of that progress has a direct bearing on signal processing. In particular, the nonlinear nature of a neuron that constitutes the basic building block of neural networks—the ability of neural networks to learn from their environments in supervised as well as unsupervised ways and the universal approximation property of neural networks—make them highly suited for solving difficult signal processing problems. Applications of neural networks include • nonlinear signal modeling, detection and estimation, and pattern classification; • system identification, adaptive filtering, and blind adaptation ; • image and speech processing; • unconventional applications. From a signal processing perspective, it is imperative that we develop proper understanding of neural network-based signal processing algorithms and how they impact the above-mentioned applications. We need to assess the impact of neural networks on the performance, robustness, and cost-effectiveness of signal processing systems and develop methodologies for integrating neural networks with other signal processing algorithms. Another important issue is how to evaluate neural network paradigms, learning algorithms, neural network structures, and identify those that work and those that do not work reliably for solving signal processing problems. The issues raised herein demand detailed attention, innovative ways of thinking, and above all, honest answers. This special issue offers a unique forum for researchers and practitioners in this field to present their views on these important questions. The response to the initial call for papers was overwhelming—a total of 101 manuscripts were submitted from all over the world. All guest editors worked hard to keep up with the schedule while maintaining the same high standard as that applied to other manuscripts submitted to the IEEE TRANSACTIONS ON SIGNAL PROCESSING. Each manuscript was reviewed by two to four anonymous reviewers, and then, the reviewers' recommendation, as well as the manuscript itself, were examined by two guest editors before a final decision was made. Based on the subject area of interest, the accepted papers are grouped into the following categories: • nonlinear signal learning and processing: theory and algorithms; • signal prediction and filtering; • blind source separation and channel equalization; • pattern classification. The first group of papers, which contain five papers and two correspondence items, concerns the theory and algorithms Publisher Item Identifier S 1053-587X(97)08049-5. for for nonlinear signal learning and processing. In " NL Theory: Checking …