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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...

In this guide designed for researchers and students of computer science, readers will find a resource for how to apply methods that work on real-life problems to their challenging applications, and a go-to work that makes fuzzy clustering issues and aspects clear.