Soft Computing pp 501-508 | Cite as

ANN – GA-Fuzzy Synergism and Its Applications

Part of the Studies in Computational Intelligence book series (SCI, volume 103)

The feed-forward backpropagation artificial neural networks (ANN) are widely used to control the various industrial processes, modelling and simulation of systems and forecasting purposes. The backpropagation learning has various drawbacks such as slowness in learning, stuck in local minima, requires functional derivative of aggregation function and thresholding function to minimize error function etc. Various researchers suggested a number of improvements in simple back-propagation learning algorithm developed.

In this paper, a program is developed for feed-forward artificial neural network with genetic algorithm (GA) as the learning mechanism to overcome some of the disadvantages of backpropagation learning mechanism to minimize the error function of ANN.

Genetic algorithm (GA) simulates the strategy of evolution and survival of fittest. It is a powerful domain free approach integrated with ANN as a learning tool. The ANN – GA integrated approach is applied to different problems to test this approach. It is well known that the GA optimization is slow and depending on the number of variables. To improve the convergence of GA, a modified GA is developed, in which, the GA parameters are modified using five fuzzy rules and concentration of genes is suggested.


Genetic Algorithm Aggregation Function Genetic Algorithm Optimization Genetic Algorithm Parameter Improve Genetic Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

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