A genetic-based fuzzy grey prediction model is proposed in this paper. Instead of working on the conventional bit by bit operation, both the crossover and mutation operators are real-valued handled by the presented algorithms. To prevent the system from turning into a premature problem, we select th
Real-valued genetic algorithms for fuzzy grey prediction system
โ Scribed by Yo-Ping Huang; Chih-Hsin Huang
- Publisher
- Elsevier Science
- Year
- 1997
- Tongue
- English
- Weight
- 807 KB
- Volume
- 87
- Category
- Article
- ISSN
- 0165-0114
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
This paper presents a fuzzy control algorithm for high order processes. The algorithm includes design of a basic fuzzy controller with its rule definition based on the qualitative reasoning in the phase plane and an incremental controller with the purpose to correspond with the order of the process.
We present a cascaded genetic algorithm which automatically generates high-performance fuzzy systems with a minimal number of fuzzy sets and rules. Such a tool is especially useful for complex systems which can no longer be designed and optimized manually. The cascade technique is tested on a fuzzy
The routine modelling method for GM 1,1 grey model is described in details and its disadvantages are pointed out. The annealing evolution algorithm derived from simulated annealing and combined with the strategy of evolution in the genetic ลฝ . algorithm is applied to GM 1,1 modelling, and the unifor
In this paper, a novel approach to adjusting the weightings of fuzzy neural networks using a Real-coded Chaotic Quantum-inspired genetic Algorithm (RCQGA) is proposed. Fuzzy neural networks are traditionally trained by using gradient-based methods, which may fall into local minimum during the learni