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.