Interner Bericht / Fachbereich Informatik, Johann Wolfgang Goethe-Universität Frankfurt a.M.
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94,12
We consider the problem of unifying a set of equations between second-order terms. Terms are constructed from function symbols, constant symbols and variables, and furthermore using monadic second-order variables that may stand for a term with one hole, and parametric terms. We consider stratified systems, where for every first-order and second-order variable, the string of second-order variables on the path from the root of a term to every occurrence of this variable is always the same. It is shown that unification of stratified second-order terms is decidable by describing a nondeterministic decision algorithm that eventually uses Makanin's algorithm for deciding the unifiability of word equations. As a generalization, we show that the method can be used as a unification procedure for non-stratified second-order systems, and describe conditions for termination in the general case.
94,13
We consider unification of terms under the equational theory of two-sided distributivity D with the axioms x*(y+z) = x*y + x*z and (x+y)*z = x*z + y*z. The main result of this paper is that Dunification is decidable by giving a non-deterministic transformation algorithm. The generated unification are: an AC1-problem with linear constant restrictions and a second-order unification problem that can be transformed into a word-unification problem that can be decided using Makanin's algorithm. This solves an open problem in the field of unification. Furthermore it is shown that the word-problem can be decided in polynomial time, hence D-matching is NP-complete.
91,1
It is well known that artificial neural nets can be used as approximators of any continous functions to any desired degree. Nevertheless, for a given application and a given network architecture the non-trivial task rests to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation. In this paper the problem is treated by an information theoretic approach. The values for the weights and thresholds in the approximator network are determined analytically. Furthermore, the accuracy of the weights and the number of neurons are seen as general system parameters which determine the the maximal output information (i.e. the approximation error) by the absolute amount and the relative distribution of information contained in the network. A new principle of optimal information distribution is proposed and the conditions for the optimal system parameters are derived. For the simple, instructive example of a linear approximation of a non-linear, quadratic function, the principle of optimal information distribution gives the the optimal system parameters, i.e. the number of neurons and the different resolutions of the variables.
89, 05
Performance and storage requirements of topology-conserving maps for robot manipulator control
(1989)
A new programming paradigm for the control of a robot manipulator by learning the mapping between the Cartesian space and the joint space (inverse Kinematic) is discussed. It is based on a Neural Network model of optimal mapping between two high-dimensional spaces by Kohonen. This paper describes the approach and presents the optimal mapping, based on the principle of maximal information gain. It is shown that Kohonens mapping in the 2-dimensional case is optimal in this sense. Furthermore, the principal control error made by the learned mapping is evaluated for the example of the commonly used PUMA robot, the trade-off between storage resources and positional error is discussed and an optimal position encoding resolution is proposed.