… In effect, the role model for soft computing is the human mind. Soft computing is based on …
Although soft computing theory and techniques were first introduced in 1980s, it has now …

… speed prediction techniques based on soft computing models (SCMs) … model can provide
up to 48% improvement in forecasting accuracy when compared to the individual ANFIS model …

… Soft computing methods are known as the essential tools that … of soft computing techniques
used in rotating electrical machines. This article presents the-state-of-the-art of soft computing …

… In this paper, a soft computing technique that uses ANN and Fuzzy Logic has been used
to establish a rainfall prediction model. This technique uses SOM to divide the data into sub-…

… The objective of this paper is to incorporate several soft computing techniques into the …
the attack type in a computer network. Among the several soft computing paradigms, neuro-fuzzy …

… , powerful training algorithms are needed to increase the models’ convergence … soft
computing models which become trapped in local optimums. As a result, soft computing models …

… On the contrary, they represent a true estimate of the real efficiency increases obtainable
from operating an existing plant with the soft-computing modeling methods used in this paper; …

… models generate more accurate forecasts. In this present study, soft computing models of …
and wavelet denoising combined with autoregressive models are developed to forecast the …

… soft computing model based on a radial basis function network (RBFN) for 24-h weather
forecasting of southern Saskatchewan, Canada. The model … (ERNN) and Hopfield model (HFM) …

… soft computing techniques to identify a model and control of its different systems. This book
is an introduction to some new fields in soft computing … , the role model for soft computing is …