 Название: New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics
Автор: Oscar Castillo, Patricia Melin
Издательство: Springer
Серия: Studies in Computational Intelligence
Год: 2024
Страниц: 422
Язык: английский
Формат: pdf (true), epub
Размер: 52.9 MB
This book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. In this book, new horizons on the theoretical developments of fuzzy logic, neural networks and optimization algorithms are envisioned. In addition, the abovementioned methods are discussed in application areas such as control and robotics, pattern recognition, medical diagnosis, decision-making, prediction and optimization of complex problems. There are a group of papers with the main theme of type-1, type-2 and type-3 fuzzy systems, which basically consists of papers that propose new concepts and algorithms based on type-1, type-2 and type-3 fuzzy logic and their applications. There is also a group of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of hybrid intelligent systems in real problems. There are also a group papers that present theory and practice of neural networks in different applications. Finally, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas.
Multi-swarm optimization is a version of swarm intelligence that uses multiple sub-swarms instead of a single swarm. An advantage of having multiple swarms is that it enables the parallel execution of the local PSO algorithms. On the other hand, this approach adds new design choices not found in a single swarm implementation. For instance, each swarm can have distinct values for the parameters controlling exploration and exploitation. The number of particles and their initial position could be distinctly defined for each swarm (i.e., not at random). We must also define how (and how often) these swarms communicate with each other. Communication enables a particular advantage of a multi-population design: when isolated swarms communicate or migrate certain particles between them, they prevent premature convergence to a local minimum. The topology of the communication channels limiting which swarms can exchange particles is also a considerable challenge. When choosing the right configuration, designers must consider two important complementary concepts: exploitation and exploration: Exploitation considers the information obtained from the best solutions found so far.
Contents:
I. Fuzzy Logic
- Fuzzy Adaptation of Parameters in a Multi-swarm Particle Swarm Optimization (PSO) Algorithm Applied to the Optimization of a Fuzzy Controller
- Fuzzifying Intrusion Detection Systems with Modified Artificial Bee Colony and Support Vector Machine Algorithms
- Type-2 Mamdani Fuzzy System Optimization for a Classification Ensemble with Black Widow Optimizer
- Towards Designing Interval Type-3 Fuzzy PID Controllers
II. Neural Networks
III. Optimization
IV. Metaheuristics: Theory and Applications
V. Applications of Intelligent Systems
VI. Hybrid Intelligent Systems
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New Horizons for Fuzzy Logic, Neural Networks and Metaheuristics 
