Abstract Quasi-Nonlinear Fuzzy Cognitive Maps (q-FCMs) generalize the classic Fuzzy
Cognitive Maps (FCMs) by incorporating a nonlinearity coefficient that is related to the …

Abstract Fuzzy Cognitive Maps (FCMs) hold promise as a mathematical tool for modeling
and simulating complex systems due to their transparency, flexibility to operate on prior …

Abstract Fuzzy Cognitive Mapping (FCM) and the extensive family of models derived from it
have firmly established their strong position in the landscape of machine learning …

This chapter lays the foundations for the book by answering two essential questions: what
are Fuzzy Cognitive Maps, and why do we use them? We show that there are three different …

Demand forecasting plays a paramount role in effective supply chain management, giving a
business the opportunity to optimize production and improve stock management and …

Long Short-term Cognitive Networks (LSTCNs) are recurrent neural networks for univariate
and multivariate time series forecasting. This interpretable neural system is rooted in …

Awooden bench has just appeared around the turn of the road. Thank god because walking
on these dunes in Hechtel-Eksel has not been easy. One would think that going for a walk …

Weather forecasting is the application of science and technology to predict atmospheric
conditions in a limited, more or less closed space. It is one of the most important functions of …

Time series forecasting is a prominent area of research, with a continuously growing focus.
Leveraging machine learning for demand forecasting, using multiple time series as …

This thesis presents my graduation research project for the master Operations Management
and Logistics (OML), conducted at Pipple BV. Moreover, it marks the end of my time as a …