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Thesis (M.S.)--Rochester Institute of Technology, 2009. / Typescript. Includes bibliographical references (leaves 56-58).

Thesis (M.S.)--West Virginia University, 1998. / "December 1998." Document formatted into pages; contains ix, 119 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 84-85).

Autonomous and semiautonomous robots are certain to play a major role in several areas in the future, from the battlefield to the household. Countless different methodologies have been applied to solve the problem of mobile robot navigation, with varying degrees of success. SAMUEL, a genetic algorithm based system for evolving semi-autonomous agent behaviors, has proven successful in generating the necessary rule sets for navigating a simple environment. Fuzzy AR TMAP (FAM) neural networks have also been applied in a similar fashion, again with success. In this thesis, a hybrid system is developed. The system fuses both SAMUEL and FAM neural networks, using SAMUEL to develop rule sets for which the FAM provides motion prediction information. The FAM motion predictor serves as an input to the genetic algorithm, so the genetic algorithm can utilize this capability without modification. A simulation using the hybrid system is developed and run, demonstrating how agents controlled by the system would respond to an example mission. The effectiveness of this approach is compared to SAMUEL's ability to complete this task unaided. Finally, open-source source code is made available.

Thesis (Ph. D.)--Michigan State University. Dept. of Civil Engineering, 2008. / Title from PDF t.p. (viewed on July 7, 2009) Includes bibliographical references (p. 196-201). Also issued in print.

Thesis (Ph. D.)--University of Texas at Austin, 2002. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 108-114). Also available on the World Wide Web.

Echo state networks represent a special type of recurrent neural networks. Recent papers stated that the echo state networks maximize their computational performance on the transition between order and chaos, the so-called edge of chaos. This work confirms this statement in a comprehensive set of experiments. Afterwards, the best performing echo state network is compared to a network evolved via neuroevolution. The evolved network outperforms the best echo state network, however, the evolution consumes significant computational resources. By combining the best of both worlds, the simplicity of echo state networks and the performance of evolved networks, a new model called locally connected echo state networks is proposed. The results of this thesis may have an impact on future designs of echo state networks and efficiency of their implementation. Furthermore, the findings may improve the understanding of biological brain tissue. 1

Aim of a work is construction of forecasting models for prediction of flood flows of measuring profile Borovnice – Dalečín on the river Svratka. As a tool for issuing predictions will be used classic hydrological forecasting models, and models based on artificial intelligence methods. Predictive model will be consisting from summer flood flows for the years 1997-2007. In the end of the work will chosen a better method for issuing forecasts