John Seiffertt

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Domains such as force protection require an effective decision maker to maintain a high level of situation awareness. A system that combines humans with neural networks is a desirable approach. Furthermore, it is advantageous for the calculation engine to operate in three learning modes: supervised for initial training and known updating, reinforcement for(More)
The Introduction to Computer Engineering course at the University of Missouri-Rolla provides a thorough understanding of basic digital logic analysis and design. The course covers: digital numbering systems, Boolean algebra, function minimization using Karnaugh maps (K-maps), memory elements, and sequential logic design. Students' grades are determined by(More)
AbstrAct As the study of agent-based computational economics and finance grows, so does the need for appropriate techniques for the modeling of complex dynamic systems and the intelligence of the constructive agent. These methods are important where the classic equilibrium analytics fail to provide sufficiently satisfactory understanding. In particular, one(More)
We propose a new research organization management paradigm to increase throughput of projects by allowing researchers to choose their own projects through self-organization. Our methods draw upon the field of Agent-Based computational social science where Artificial Life and simulated societies have been used to study complex systems including economies and(More)
Backpropagation is the most widely used neural network learning technique. It is based on the mathematical notion of an ordered derivative. In this paper, we present a formulation of ordered derivatives and the backpropagation training algorithm using the important emerging area of mathematics known as the time scales calculus. This calculus, with its(More)
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