Evolving neuro-fuzzy systems with kernel activation functions

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 adaptive method of ensembles’ tuning for neural networks with kernel activation functions which are learnt on both optimization procedures and memory (that gives an optimal accuracy of an ensemble output signal). Speaking of processing data streams in an online mode we introduced a method for ensembles’ tuning for neuro-fuzzy systems with kernel activation functions which are learnt on both optimization procedures and memory (that provides a high accuracy of an ensemble output signal on the basis of fuzzy generalization).