Download Neural networks for control by W Thomas Miller; Richard S Sutton; Paul J Werbos; National PDF

By W Thomas Miller; Richard S Sutton; Paul J Werbos; National Science Foundation (U.S.)

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Despite these complexities, which make it impossible to prove global convergence results analogous to those existing for linear models, the on-line estimation of parameters for non­ linear models is being taken up by connectionists with great enthusiasm. 6 This algorithm is and Rumelhart, Hinton and Williams based on the class of nonlinear models consisting of layered networks of units that apply a differentiable nonlinear "squashing function" to the weighted sum of their inputs. Due to the layered structure of these net­ works, this class of models consists of functions that can be constructed by recursively composing "squashed" weighted sums with other com­ positions of squashed weighted sums, etc.

The relationship between Connectionist Learning for Control 27 parameter estimation for forming models, or rules, and for implementing memory structures. 9) . The parameters are the contents of the memory locations, and the estimation process just amounts to storing information in specific locations. In the simplest case of noise-free training data, the learning rate, or gain, of the estimation method can be set so as to accomplish one-trial learning because there is never any interference between the stored items.

We cannot say that the method necessarily discovers the "rule" by which the data set was generated. Reliable generalization requires that either the number of training examples is many times larger than the number of parameters, 7 or the class of parameterized models is already matched in some way to the generation rule. 7 B aum and H aussler (1989) provide a theoretical analysis of t he relationship be­ tween the number of weights, the number of training examples, and the accuracy of generalization for layered n units.

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