Eine neue Methode des unscharfen Schließens für Expertensysteme

Many cardiac diseases are treated with electronic implants today. Typical implants are pacemakers and implantable cardioverter defibrillators (ICD). As a preferable therapeutic treatment the implants have been developed to highly programmable electronic devices in the recent decades. CWA GmbH, Aachen, Germany, develops in cooperation with the „Innere Medizin I” of the clinical center in Aachen, Germany, and the BIOTRONIK GmbH, Berlin, Germany, expert systems for programming such devices, enabling the physicians to program the devices by using their medical jargon rather than in terms of technical parameters. Thus, devices programming without detailed technical know how on the devices is possible. Topic of this thesis is the modeling and implementation of unprecise human expert knowledge within expert systems. Existing techniques are presented, above all the Bayes theorem, the Dempster Shafer theory and the theory of Certainty Factors. As one of the most important techniques to implement unprecise knowledge the Fuzzy Set Theory and Fuzzy Control in particular are discussed more detailed. The Fuzzy methods do not only distinguish themselves as important and suitable techniques, but show up with some problems when dealing with huge knowledge bases with hundreds of rules too. These problems are presented and discussed in detail. A new idea of this thesis is an extension to the existing Fuzzy Set Theory, which provides the possibility to efficiently implement, verify and validate unprecise knowledge in huge knowledge bases, while simultaneously increasing the accuracy (in the sense of the formulated human knowledge) and reducing the required calculation power. The new technique is called Scalar Fuzzy Control. It provides the possibility to directly implement human expert knowledge which deals with real data values (i.e. scalar values), instead of transforming them into multi-dimensional linguistic variables and matrices. In order to implement unprecise knowledge on the scalar values, unprecise comparison and combination operators are derived and defined. Their properties are discussed in detail and important theorems are set up and proofed. The subject of expert systems for programming pacemakers and ICDs is used to demonstrate the practical usage of the new technique. The thesis gives a short introduction to cardiac diseases and therapeutic treatments first. By using the Scalar Fuzzy Control the implementation of unprecise knowledge is demonstrated and the results are compared to implementations using the „conventional” Fuzzy Control. The implementation was inspected and proved in cooperation with physicians from the „Innere Medizin I” of the clinical center in Aachen. All in all it is shown that the Scalar Fuzzy Control is a valuable technique to implementing unprecise knowledge

Optimization of combine processes using expert knowledge and methods of artificial intelligence

Combine harvesters are used to gather plants from the field and separate them into the components of value, the grain and the straw. The optimal utilization of existing combine potential is an inevitable task to maximize harvest efficiency and hence to maximize profit. The only way to optimize the threshing and separation processes during harvest is to adjust the combine settings to existing conditions. Operating permanently at optimal harvest efficiency can only be achieved by an automatic control system. However, for reasons of transparency and due to lack of sensors, the approach in this thesis is a combined development of an interactive and an automatic control system for combine process optimization. The optimization of combine processes is a multi-dimensional and multi-objective optimization problem. The objectives of optimization are the harvest quality parameters. The decision variables, the parameters that can be modified, are the combine settings. Analytical optimization methods require the existence of a model that provides function values in dependence of defined input parameters. A comprehensive quantitative model for the input-output-behavior of the combine does not exist. Alternative optimization methods that handle multi-dimensional and multi-objective optimization problems can be found in the domain of Artificial Intelligence. In this work, knowledge acquisition was performed in order to obtain expert knowledge on combine process optimization. The result is a knowledge base with six adjustment matrices for different crop and combine types. The adjustment matrices contain problem oriented setting adjustment recommendations in order to solve single issues with quality parameters. A control algorithm has been developed that is also capable of solving multiple issues at the same time, utilizing the acquired expert knowledge. The basic principle to solve the given multi-objective optimization problem is a transformation into one-dimensional single-objective optimization problems which are solved iteratively. Several methods have been developed that are applied sequentially. In simulation, the average improvement from initial settings to optimized settings, achieved by the control algorithm, is between 34.5 % and 67.6 %. This demonstrates the good performance of the control algorithm