This book focuses on the fields of neural networks swarm optimization algorithms clustering
and fuzzy logic. This book describes a hybrid method with three different techniques of
intelligence computation: neural networks optimization algorithms and fuzzy logic. Within the
neural network techniques competitive neural networks (CNNs) are used for the optimization
algorithms technique we used the fireworks algorithm (FWA) and in the area of fuzzy logic
the Type-1 Fuzzy Inference Systems (T1FIS) and the Interval Type-2 Fuzzy Inference Systems
(IT2FIS) were used with their variants of Mamdani and Sugeno type respectively. FWA was
adapted for data clustering with the goal to help of competitive neural network to find the
optimal number of neurons. It is important to mention that two variants were applied to the
FWA: dynamically adjust of parameters with Type-1 Fuzzy Logic (FFWA) as the first one and
Interval Type-2 (F2FWA) as the second one. Subsequently based on the outputs of the CNN and
with the goal of classification data we designed Type-1 and Interval Type-2 Fuzzy Inference
Systems of Mamdani and Sugeno type. This book is intended to be a reference for scientists and
engineers interested in applying a different metaheuristic or an artificial neural network in
order to solve optimization and applied fuzzy logic techniques for solving problems in
clustering and classification data. This book is also used as a reference for graduate courses
like the following: soft computing swarm optimization algorithms clustering data fuzzy
classify and similar ones. We consider that this book can also be used to get novel ideas for
new lines of research new techniques of optimization or to continue the lines of the research
proposed by the authors of the book.
