<p><p>In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus
Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation
โ Scribed by Daniela Sanchez, Patricia Melin
- Publisher
- Springer
- Year
- 2017
- Tongue
- English
- Leaves
- 104
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
<p><p>This book focuses on the fields of fuzzy logic, granular computing and also considering the control area. These areas can work together to solve various control problems, the idea is that this combination of areas would enable even more complex problem solving and better results. In this book
Provides an up-to-date integration of expert systems with fuzzy logic and neural networks. * Includes coverage of simulation models not present in other books. * Presents cases and examples taken from the authors' experience in research and applying the technology to real-world situations.
Provides an up-to-date integration of expert systems with fuzzy logic and neural networks.Includes coverage of simulation models not present in other books.Presents cases and examples taken from the authors' experience in research and applying the technology to real-world situations.
<p><p>This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms
<p><p>This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms