Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder. Despite its apparent computational advantage over humans, no machine or computer has come close to achieving the level of sensor-based control which humans are capable of. Thus, there is a clear need to develop computational methods which can abstract human decision-making processes based on sensory feedback.
Neural networks offer one such method with their ability to map complex nonlinear functions.
In this paper, we examine the potential of an efficient neural network learning architecture to the problems of system identification and control. The cascade two learning architecture dynamically adjusts the size of the network as part of the learning process. As such, it allows different units to have different activation functions, resulting in faster learning, smoother approximations, and fewer required hidden units. We use the methods discussed here towards identifying human control strategy. For updates and comments, please see these instructions .