The power of knowledge acquisition systems that employ failure-driven learning derives from two main sources: an effective global credit assignment process that determines when to acquire new knowledge by watching an expert’s behavior, and an efficient local credit assignment process that determines what new knowledge will be created for completing a failed explanation of an expert’s action. Be...

Fuzzy modeling has many advantages over the non-fuzzy methods, such as robustness against uncertainties and less sensitivity to the varying dynamics of nonlinear systems. Data-driven fuzzy modeling needs to extract fuzzy rules from the input/output data, and train the fuzzy parameters. This paper takes advantages from deep learning, probability theory, fuzzy modeling, and extreme learning machi...

An iterative backward selection method for determination of relevant input variables in data-driven fuzzy modeling is presented. The method utilizes parameters of the Takagi-Sugeno model as a factor to determine the significance of input variables. As a result, it is less computationally intensive than most of the existing methods for input variable selection.

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with...