Abstract: This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine. Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upp...

Due to the highly nonlinearity of the flux-linkage characteristics of Switched Reluctance Motor drives (SRM), accurately modelling is cumbersome. In this paper, the offlinetrained and the online-trained Radial Basis function (RBF) neural network model are proposed for estimating the SRM flux-linkage under running conditions. To investigate the performance of the modelling schemes, the simulatio...

Modelling a peer assessment using IMS LD and IMS QTI is difficult for average practitioners. In this paper, we apply domain-specific modelling technologies to develop a peer assessment modelling language, in which notations are directly chosen from the concepts and rules used to describe peer assessment. Thus, practitioners can easily understand such a high-level language and use it to specify ...

We are interested in capturing time series generated by small wireless electronic sensors. Battery-operated sensors must avoid heavy use of their wireless radio which is a key cause of energy dissipation. When many sensors transmit, the resources of the recipient of the data are taxed; hence, limiting communication will benefit the recipient as well. In our paper we show how time series generat...

Understanding why students stopout will help in understanding how students learn in MOOCs. In this report, part of a 3 unit compendium, we describe how we build accurate predictive models of MOOC student stopout. We document a scalable, stopout prediction methodology, end to end, from raw source data to model analysis. We attempted to predict stopout for the Fall 2012 offering of 6.002x. This i...