Evolving fuzzy neural networks in adaptive knowledge bases to support task-oriented decision making for sensor management

In the area of process refinement under level 4 data fusion of the JDL model [1,2], high-level sensor management is often performed by human operators manning sensor systems who constantly have to monitor the situational and sensor picture for critical events and dynamically employ myriad sensors' functions to carry out mission-specific tasks. To assist the human operators in dealing better with the intense pressure to perform effectively in such environments, adaptive knowledge bases capable of capturing human operators' behavioural patterns can be harnessed to augment the task-oriented decision making process of sensor management. However, the unique problem domain in which human operators exercise sensor management functions has direct impact on obtainable training data and imposes several performance requirements on the adaptive knowledge bases. Several rule-learning algorithms [4-7] do not readily fulfil the identified requirements and selecting a more suitable alternative constitutes the focus of this paper. This paper selects the adaptive online-learning evolving fuzzy neural network (EFUNN) [8,9] and details two algorithmic and one qualitative contribution that enhance EFUNN's ability to realize the construction of adaptive knowledge bases. The two algorithmic contributions consist of modifications of EFUNN's original learning mechanism to handle training records with outlying inputs and those with contradictory class outputs that characterise the obtainable training data. The qualitative contribution suggests how multiple EFUNNs can be mapped to respective task-oriented rule-sets giving rise to adaptive knowledge bases that assist the human operators in selecting the right observation tasks.