By Jordan M.
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55 Reference Models Throughout this chapter we have characterized controllers as systems that invert the plant dynamics. For example, a predictive controller was characterized as an inverse model of the plant|a system that maps desired plant outputs into the corresponding plant inputs. This mathematical ideal, however, is not necessarily realizable in all situations. One common di culty arises from the presence of constraints on the magnitudes of the control signals. An ideal inverse model moves the plant to an arbitrary state in a small number of time steps, the number of steps depending on the order of the plant.
The perceptron learns to assign a binary category label to each of a set of input patterns. For example, the input pattern might represent the output of a motion detection stage in the visual system and the binary label might specify whether or not an object can be caught before it falls to the ground. The perceptron is provided with examples of input patterns paired with their corresponding labels. The goal of the learning procedure is to extract information from the examples so that the system can generalize appropriately to novel data.
Schmidt, R. A. 1975. A schema theory of discrete motor skill learning. Psychological Review, 82, 225-260. Specht, D. F. 1991. A general regression neural network. IEEE Transactions on Neural Networks, 2, 568-576. Turvey, M. , Shaw, R. , & Mace, W. 1978. Issues in the theory of action: Degrees of freedom, coordinative structures and coalitions. In J. , Attention and Performance, VII. Hillsdale, NJ: Erlbaum. 63 Wahba, G. 1990. Spline models for observational data. Philadelphia, PA: SIAM.
Computational aspects of motor control and motor learning by Jordan M.