Application of Neural Networks in High Assurance Systems: A Survey

Artificial Neural Networks (ANNs) are employed in many areas of industry such as pattern recognition, robotics, controls, medicine, and defence. Their learning and generalization capabilities make them highly desirable solutions for complex problems. However, they are commonly perceived as black boxes since their behavior is typically scattered around its elements with little meaning to an observer. The primary concern in safety critical systems development and assurance is the identification and management of hazards. The application of neural networks in systems where their failure can result in loss of life or property must be backed up with techniques to minimize these undesirable effects. Furthermore, to meet the requirements of many statutory bodies such as FAA, such a system must be certified. There is a growing concern in validation of such learning paradigms as continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure a reliable system performance. In this paper, we survey the application of neural networks in high assurance systems that have emerged in various fields, which include flight control, chemical engineering, power plants, automotive control, medical systems, and other systems that require autonomy. More importantly, we provide an overview of assurance issues and challenges with the neural network model based control scheme. Methods and approaches that have been proposed to validate the performance of the neural networks are outlined and discussed after a comparative examination.