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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...

This monograph is devoted to the consideration of new deep neural networks – Evolving Hybrid Stacking Neuro-Neo-Fuzzy Systems of Artificial Intelligence based on Group Method of Data Handling, which, in turn, is the first known method of deep learning. This method is based on the principle of self-organization and, unlike in other deep learning methods, it allows not only to adjust the weights of neural connections, but also to synthesize the optimal network structure in online mode. The proposed approach allows you to process information in online mode and solve a wide class of Data Stream...

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.