More recently, ANNs and fuzzy set theoretic approach have been proposed for many different industrial applications. A number of papers have been published in the last two decades. An illustrative list is given in bibliography. Both techniques have their own advantages and disadvantages. The integration of these approaches can give improved results.
In the previous chapter, the performance aspect of ANN has been discussed in detail. To overcome some of the problems of ANN and improve its training and testing performance, the simple neuron is modified and a generalized neuron is developed in this chapter.
In the common neuron model generally the aggregation function is summation, which has been modified to obtain a generalized neuron (GN) model using fuzzy compensatory operators as aggregation operators to overcome the problems such as large number of neurons and layers required for complex function approximation, which not only affect the training time but also the fault tolerant capabilities of the artificial neural network (ANN) (Chaturvedi 1997).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2008). Development of Generalized Neuron and Its Validation. In: Soft Computing. Studies in Computational Intelligence, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77481-5_5
Download citation
DOI: https://doi.org/10.1007/978-3-540-77481-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77480-8
Online ISBN: 978-3-540-77481-5
eBook Packages: EngineeringEngineering (R0)