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- Nathan Brannon, John Seiffertt, Timothy Draelos, Donald C. Wunsch
- Neural Networks
- 2009

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)

- John Seiffertt, Donald C. Wunsch
- IEEE Transactions on Neural Networks
- 2010

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)

- Nathan Brannon, Gregory Conrad, Timothy Draelos, John Seiffertt, Donald C. Wunsch
- The 2006 IEEE International Joint Conference on…
- 2006

For applications such as force protection, an effective decision maker needs to maintain an unambiguous grasp of the environment. Opportunities exist to leverage computational mechanisms for the adaptive fusion of diverse information sources. The current research involves the use of neural networks and Markov chains to process information from sources… (More)

- J. Seiffertt, D. Wunsch
- IEEE Computational Intelligence Magazine
- 2008

Evolution, human and animal cognition, and the emergent coordination of systems of autonomous agents are among the areas of nature drawn upon for inspiration by the field of computational intelligence. Researchers are increasingly using these aspects of nature in the exploration of market interaction, both to develop more scientific knowledge about economic… (More)

- John Seiffertt, Samuel A. Mulder, Rohit Dua, Donald C. Wunsch
- 2009 International Joint Conference on Neural…
- 2009

The study of strategic interaction among a society of agents is often handled using the machinery of game theory. This research examines how a Markov Decision Process (MDP) model may be applied to an important element of repeated game theory: the iterated prisoner's dilemma. Our study uses a Markovian approach to the game to represent the problem of in a… (More)

- John Seiffertt
- 2009 International Joint Conference on Neural…
- 2009

The time scales calculus, which includes the study of the alpha derivative, is an emerging key area in mathematics. We extend this calculus to Approximate Dynamic Programming. In particular, we investigate application of the alpha derivative, one of the fundamental dynamic derivatives of time scales. We present a alpha-derivative based derivation and proof… (More)

- Paul Robinette, John Seiffertt, Ryan J. Meuth, Ryanne Dolan, Donald C. Wunsch
- 2009 International Joint Conference on Neural…
- 2009

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)

- John Seiffertt
- 2017

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