An interactive weight space reduction procedure for nonlinear multiple objective mathematical programming

Mackin, Patrick D.; Roy, Asim; Wallenius, Jyrki

doi:10.1007/s10107-009-0293-6

Cited by 5 publications

(1 citation statement)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [10] and [43], acquisition systems to obtain real data of wind turbines and wind farms are used for the fault detection. An interactive method and the underlying theory for solving multiple objective mathematical programming problems is introduced in [24]. The paper [22] demonstrates the concept of developing a cellular computational networks using dimensionality reduction in a dynamic recurrent network for scalability and better performance for the dynamics learning of large networked systems.…”

Section: Introductionmentioning

confidence: 99%

MSAFIS: an evolving fuzzy inference system

Rubio

Bouchachia

2015

Soft Comput

View full text Add to dashboard Cite

In this paper, the problem of learning in big data is considered. To solve this problem, a new algorithm is proposed as the combination of two important evolving and stable intelligent algorithms: the sequential adaptive fuzzy inference system (SAFIS), uniform stable backpropagation algorithm (SBP). The modified sequential adaptive fuzzy inference system (MSAFIS) is the SAFIS with the difference that the SBP is used instead of the Kalman filter for the updating of parameters. The SBP has the advantage that is obtains a better learning than the Kalman filter in big data. The effectiveness of the studied methods is verified by two experiments.

show abstract