Development of Cognitive Capabilities for Smart Home using a Self-Organizing Fuzzy Neural Network

A smart home requires cognitive assistance to analyze and understand the behavior in this sensory rich environment. In this paper we explore the potential of a self-organizing fuzzy neural network (SOFNN) as a core component of a cognitive system for a smart home environment. We develop a cognitive reasoning module that has the ability to adapt its neuronal structure through adding and pruning of neurons according to the incoming data. The SOFNN rules explore the relations of the inputs and the desired reasoning outputs. The network is trained with realistic synthesized data to show its adaptation capability and is tested with unseen data to validate its cognitive capabilities. We outline the theoretical development and describe the results achieved. This initial implementation of the cognitive module demonstrates the potential of the architecture and will serve as a very important test-bed for future work.