Titlebook: Uncertainty Analysis in Engineering and Sciences: Fuzzy Logic, Statistics, and Neural Network Approa; Bilal M. Ayyub,Madan M. Gupta Book 1

誘導(dǎo)

CAPE

Hybrid Least-Squares Regression Analysispter. Both bivariate regression model and multiple regression model are derived and developed. Two numerical examples are used to demonstrate the proposed method. In each example, hybrid regression equations and their reliability measures are calculated. Furthermore, hybrid least-squares regression

代替

Linear Regression With Random Fuzzy Numbersoaches are discussed: The first one is an application of Zadeh’s extension principle to optimal classical estimators, but unfortunately they lose their optimality. The second one is the attempt to develop some kind of best linear estimates, but unfortunately fuzzy sets do not constitute a linear spa

Malleable

RALES

誹謗

Fuzzy Genetic Algorithm Based Approach to Machine Learning Under Uncertaintyurposes. In this chapter, an approach for symbolic knowledge extraction from noisy raw process data using genetic algorithms is illustrated. The applicability of the developed system for fault diagnosis is shown on a hydrocarbon chlorination plant.

細(xì)絲

Inflammation

Adaptive Fuzzy Systems With Sinusoidal Membership Functionsks and a fuzzy system with a singleton fuzzifier, a product inference, a centroid defuzzifter, and a sinusoidal membership function is discussed in this paper. Fuzzy basis function expansions of a multi-input and singal output (MISO) fuzzy systems are given in order to describe the input-output rela

楓樹(shù)

暖昧關(guān)系