The aim of this book is to develop new methods for adaptive learning for evolving neural networks and neuro-fuzzy systems with kernel activation functions. The book provides an overview on different principles of neural networks' learning, basic and most popular neural networks and neuro-fuzzy systems where kernel constructions are used as activation functions. Advantages and shortcomings are defined for well-known approaches. The proposed evolving architectures can be used for time series' and data streams' processing. Taking into consideration all the mentioned above, we proposed an...