Abstract
We have proposed the forward‐propagation rule (FP) as an inverse model learning scheme from the viewpoint of biological motor control. This learning scheme is based on a Newton‐like method, by which multilayered neural network can acquire an inverse model of the controlled object by a small number of iterative learning trials. There is a problem, however, that the learning procedure, which is characterized by estimation of the supervisor's signal for the input–output signal of the neuron, and also the solution of a linear multiple regression problem for updating the connection weights, is complicated, making it difficult to analyze the learning process. This paper introduces the correlation of the propagated error signal from the viewpoint of the maximum‐likelihood method in order to realize a goal‐directed learning, which has not hitherto been considered in FP, and extends the learning rule to the generalized least‐square method. As a result, it is clearly shown that the learning rule in FP is an approximate gradient method. The learning ability of the method is demonstrated by computer simulation. The proposed procedure contains a regularization term derived from the logarithmic likelihood, and the behavior after the convergence of learning exhibits a more stable tendency than in the conventional method. It is also shown that learning can be performed by a simplified method in which the error is simply propagated in the forward direction. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(13): 54–66, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20484