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Articles 31 - 36 of 36
Full-Text Articles in Systems Engineering
Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding
Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding
Electrical & Computer Engineering Faculty Publications
Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …
Computational Model For Neural Architecture Search, Ram Deepak Gottapu
Computational Model For Neural Architecture Search, Ram Deepak Gottapu
Doctoral Dissertations
"A long-standing goal in Deep Learning (DL) research is to design efficient architectures for a given dataset that are both accurate and computationally inexpensive. At present, designing deep learning architectures for a real-world application requires both human expertise and considerable effort as they are either handcrafted by careful experimentation or modified from a handful of existing models. This method is inefficient as the process of architecture design is highly time-consuming and computationally expensive.
The research presents an approach to automate the process of deep learning architecture design through a modeling procedure. In particular, it first introduces a framework that treats …
Human-Intelligence And Machine-Intelligence Decision Governance Formal Ontology, Faisal Mahmud
Human-Intelligence And Machine-Intelligence Decision Governance Formal Ontology, Faisal Mahmud
Engineering Management & Systems Engineering Theses & Dissertations
Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in …
Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter
Human-Intelligence/Machine-Intelligence Decision Governance: An Analysis From Ontological Point Of View, Faisal Mahmud, Teddy Steven Cotter
Engineering Management & Systems Engineering Faculty Publications
The increasing CPU power and memory capacity of computers, and now computing appliances, in the 21st century has allowed accelerated integration of artificial intelligence (AI) into organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational processes including medical diagnosis, automated stock trading, integrated robotic production systems, telecommunications routing systems, and automobile fuzzy logic controllers. Self-driving automobiles are just the latest extension of AI. This thrust of AI into organizations and everyday life rests on the AI community’s unstated assumption that “…every aspect of human learning and intelligence could be so precisely described …
Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal
Quantum Inspired Algorithms For Learning And Control Of Stochastic Systems, Karthikeyan Rajagopal
Doctoral Dissertations
"Motivated by the limitations of the current reinforcement learning and optimal control techniques, this dissertation proposes quantum theory inspired algorithms for learning and control of both single-agent and multi-agent stochastic systems.
A common problem encountered in traditional reinforcement learning techniques is the exploration-exploitation trade-off. To address the above issue an action selection procedure inspired by a quantum search algorithm called Grover's iteration is developed. This procedure does not require an explicit design parameter to specify the relative frequency of explorative/exploitative actions.
The second part of this dissertation extends the powerful adaptive critic design methodology to solve finite horizon stochastic optimal …
Exploring Knowledge Processes For Technology Assimilation, Rochelle K. Young
Exploring Knowledge Processes For Technology Assimilation, Rochelle K. Young
Engineering Management & Systems Engineering Theses & Dissertations
In the emerging knowledge society, the ability to make the experience and expertise of those involved in and affected by new technology unconditionally available to all members of an organization is becoming increasingly important. One of the problems in developing such knowledge processes for technology assimilation is that current social structures do not easily accommodate unconditional participation. Since the implementation of modern information technology is changing the workplace and the nature of work itself, alternative social structures are needed. This research takes as given that deep questions concerning knowledge processes and social transformation are in principle undecidable; and, only questions …