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 prese
nted algorithms. To prevent the system from turning into a premature p
roblem, we select the elitists from two groups of possible solutions t
o reproduce the new populations. To verify the effectiveness of the pr
oposed genetic algorithms, two simple functions are first tested. The
results show that our method outperforms the conventional one no matte
r whether from the viewpoint of the number of iterations required to f
ind the optimum solutions or from the final solutions obtained. The re
al-valued genetic algorithms are then exploited to optimize the fuzzy
controller which is designed to perform the compensation job. Two diff
erent types of fuzzy inference rules are considered to compensate for
the predicted errors from the grey model. The difficulty encountered i
n applying the genetic algorithms to adjusting the fuzzy parameters is
also discussed. Based on the simulation results from the problems of
the weather forecast, we found that the proposed methodology is very e
ffective in determining the quantity of compensation for the predicted
outputs from the traditional grey approach. (C) 1997 Elsevier Science
B.V.