Neural-Network-Based Fuzzy Logic Control and Decision System
IEEE Transactions on Computers - Special issue on artificial neural networks
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
On the principles of fuzzy neural networks
Fuzzy Sets and Systems
A new approach of multi-stage fuzzy logic inference
Fuzzy Sets and Systems
Compensatory Genetic Fuzzy Neural Networks and Their Applications
Compensatory Genetic Fuzzy Neural Networks and Their Applications
Neural Fuzzy Control Systems with Structure and Parameter Learning
Neural Fuzzy Control Systems with Structure and Parameter Learning
On multistage fuzzy neural network modeling
IEEE Transactions on Fuzzy Systems
Constructive granular systems with universal approximation and fast knowledge discovery
IEEE Transactions on Fuzzy Systems
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
Granular neural networks for numerical-linguistic data fusion and knowledge discovery
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
With ever-improving information technologies and high performance computational power, recent techniques in granular computing, soft computing and cognitive science have allowed an increasing understanding of normal and abnormal brain functions, especially in the research of human's pattern recognition by means of computational intelligence. It is well understood that normal brains have high intelligence to recognize different geometrical patterns, but a systematic framework of biological neural network has not yet be established. In this paper, we propose the genetic granular cognitive fuzzy neural networks (GGCFNN) in order to efficiently testify artificial neural networks' learning capability on human's pattern recognition in term of symmetric and similar geometry patterns. In contrast to other information systems, the GGCFNN is a highly hybrid intelligent system integrating the techniques of genetic algorithms, granular computing, and fuzzy neural networks with cognitive science for pattern recognition. Our ability to simulate biological neural networks makes it possible a more comprehensive quantitative analysis on the pattern recognition of human brains, and our preliminary experiment results would shed lights on the future research of cognitive science and brain informatics.