Название: Blind Equalization in Neural Networks: Theory, Algorithms and Applications
Автор: Liyi Zhang, Yunshan Sun
Издательство: De Gruyter
ISBN: 3110449625
Год: 2018
Страниц: 256
Язык: английский
Формат: epub
Размер: 34.0 MB

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms in recurrent networks, fuzzy networks and other frequently-studied neural networks. Each algorithm is accompanied by derivation, modeling and simulation, making the book an essential reference for electrical engineers, computer intelligence researchers and neural scientists.

Blind equalization (BE) technology is a new adaptive technology. BE only uses the prior information of received signals to equalize the channel characteristics, so training sequence is not needed. The output sequence is close to the transmitted sequence. Inter-symbol interference is overcome effectively and the quality of communication is improved by BE. Neural network (NN) is a cross-edge discipline of neural science, information science, and computer science. NN has the following abilities such as massively parallel, distributed storage and processing, self-organizing, adaptive, self-learning, and highly fault tolerant. The combination of NN and BE can improve convergence performance and equalization effect. The combination of NN and BE is a hot research topic in communication, signal, and information processing. It has important theoretical significance and practical value.

The NN and BE algorithms are combined, and the new neural network is systematically studied. Some research results have been published in important academic journals and also in international and domestic conferences. This book is a summary of the results of these studies. The latest research trends and frontiers in neural network blind equalization algorithm in domestic and international are reflected basically in the book.