Soft Computing for Hybrid Intelligent Systems
Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. This edited book comprises papers on diverse aspects of soft computing and hybrid intelligen...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
2008, 2008
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| Edition: | 1st ed. 2008 |
| Series: | Studies in Computational Intelligence
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Modeling and Simulation of the Defuzzification Stage Using Xilinx System Generator and Simulink
- Modeling, Simulation and Optimization
- A New Evolutionary Method Combining Particle Swarm Optimization and Genetic Algorithms Using Fuzzy Logic
- A Hybrid Learning Algorithm for Interval Type-2 Fuzzy Neural Networks: The Case of Time Series Prediction
- Optimization of Artificial Neural Network Architectures for Time Series Prediction Using Parallel Genetic Algorithms
- Optimized Algorithm of Discovering Functional Dependencies with Degrees of Satisfaction Based on Attribute Pre-scanning Operation
- A Fuzzy Symbolic Representation for Intelligent Reservoir Well Logs Interpretation
- How to Solve a System of Linear Equations with Fuzzy Numbers
- Design and Implementation of a Hybrid Fuzzy Controller Using VHDL.
- A New Biometric Recognition Technique Based on Hand Geometry and Voice Using Neural Networks and Fuzzy Logic
- Intelligent Agents and Social Systems
- A Hybrid Model Based on a Cellular Automata and Fuzzy Logic to Simulate the Population Dynamics
- Soft Margin Training for Associative Memories: Application to Fault Diagnosis in Fossil Electric Power Plants
- Social Systems Simulation Person Modeling as Systemic Constructivist Approach
- Modeling and Simulation by Petri Networks of a Fault Tolerant Agent Node
- Fuzzy Agents
- Hardware Implementations
- Design and Simulation of the Fuzzification Stage through the Xilinx System Generator
- High Performance Parallel Programming of a GA Using Multi-core Technology
- Scalability Potential of Multi-core Architecture in a Neuro-Fuzzy System
- Methodology to Test and Validate a VHDL Inference Engine through the Xilinx System Generator
- Intelligent Control
- Optimization of Interval Type-2 Fuzzy Logic Controllers for a Perturbed Autonomous Wheeled Mobile Robot Using Genetic Algorithms
- Fuzzy Control for Output Regulation of a Servomechanism with Backlash
- Stability on Type-1 and Type-2 Fuzzy Logic Systems
- Comparative Study of Type-1 and Type-2 Fuzzy Systems Optimized by Hierarchical Genetic Algorithms
- Comparison between Ant Colony and Genetic Algorithms for Fuzzy System Optimization
- Pattern Recognition
- Type-1 and Type-2 Fuzzy Inference Systems as Integration Methods in Modular Neural Networks for Multimodal Biometry and Its Optimization with Genetic Algorithms
- Interval Type-2 Fuzzy Logic for Module Relevance Estimation in Sugeno Integration of Modular Neural Networks
- Optimization of Response Integration with Fuzzy Logic in Ensemble Neural Networks Using Genetic Algorithms
- Optimization of Modular Neural Network, Using Genetic Algorithms: The Case of Face and Voice Recognition