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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Special issue on computational intelligence algorithms and applications

Neagu, Daniel 12 July 2016 (has links)
Yes
2

Studies on real-valued negative selection algorithms for self-nonself discrimination a thesis /

Dixon, Shane, Yu, Xiao-Hua. January 1900 (has links)
Thesis (M.S.)--California Polytechnic State University, 2010. / Title from PDF title page; viewed on February 22, 2010. Major professor: Xiao-Hua (Helen) Yu, Ph.D. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "February, 2010." Includes bibliographical references (p. 90-91).
3

Automated optical cluster tracking of polymorphic targets in complex and occluded environments /

Cilia, Andrew. January 2007 (has links)
Thesis (Ph.D.)--University of Texas at Dallas, 2007. / Includes vita. Includes bibliographical references (leaves 183-192)
4

An Analysis of Parameter Control Mechanisms for the Particle Swarm Optimization Algorithm

Harrison, Kyle Robert 07 1900 (has links)
The particle swarm optimization (PSO) algorithm is a stochastic, population-based optimization technique influenced by social dynamics. It has been shown that the performance of the PSO algorithm can be greatly improved if the control parameters are appropriately tuned. However, the tuning of control parameter values has traditionally been a time-consuming, empirical process followed by statistical analysis. Furthermore, ideal values for the control parameters may be time-dependent; parameter values that lead to good performance in an exploratory phase may not be ideal for an exploitative phase. Self-adaptive algorithms eliminate the need to tune parameters in advance, while also providing real-time behaviour adaptation based on the current problem. This thesis first provides an in-depth review of existing self-adaptive particle swarm optimization (SAPSO) techniques. Their ability to attain order-2 stability is examined and it is shown that a majority of the existing SAPSO algorithms are guaranteed to exhibit either premature convergence or rapid divergence. A further investigation focusing on inertia weight control strategies demonstrates that none of the examined techniques outperform a static value. This thesis then investigates the performance of a wide variety of PSO parameter configurations, thereby discovering regions in parameter space that lead to good performance. This investigation provides strong empirical evidence that the best values to employ for the PSO control parameters change over time. Finally, this thesis proposes novel PSO variants inspired by results of the aforementioned studies. / Thesis (PhD)--University of Pretoria, 2018. / Computer Science / PhD / Unrestricted
5

Transformation-invariant topology preserving maps

McGlinchey, Stephen John January 2000 (has links)
No description available.
6

Application of computational intelligence to power system security assessment /

Jensen, Craig A., January 1999 (has links)
Thesis (Ph. D.)--University of Washington, 1999. / Vita. Includes bibliographical references (leaves 154-159).
7

Genetic Programming Approach for Nonstationary Data Analytics

Kuranga, Cry 16 February 2021 (has links)
Nonstationary data with concept drift occurring is usually made up of different underlying data generating processes. Therefore, if the knowledge of the existence of different segments in the dataset is not taken into consideration, then the induced predictive model is distorted by the past existing patterns. Thus, the challenge posed to a regressor is to select an appropriate segment that depicts the current underlying data generating process to be used in a model induction. The proposed genetic programming approach for nonstationary data analytics (GPANDA) provides a piecewise nonlinear regression model for nonstationary data. The GPANDA consists of three components: dynamic differential evolution-based clustering algorithm to split the parameter space into subspaces that resemble different data generating processes present in the dataset; the dynamic particle swarm optimization-based model induction technique to induce nonlinear models that describe each generated cluster; and dynamic genetic programming that evolves model trees that define the boundaries of nonlinear models which are expressed as terminal nodes. If an environmental change is detected in a nonstationary dataset, a dynamic differential evolution-based clustering algorithm clusters the data. For the clusters that change, the dynamic particle swarm optimization-based model induction approach adapts nonlinear models or induces new models to create an updated genetic programming terminal set and then, purple the genetic programming evolves a piecewise predictive model to fit the dataset. To evaluate the effectiveness of GPANDA, experimental evaluations were conducted on both artificial and real-world datasets. Two stock market datasets, GDP and CPI were selected to benchmark the performance of the proposed model to the leading studies. GPANDA outperformed the genetic programming algorithms designed for dynamic environments and was competitive to the state-of-art-techniques. / Thesis (PhD)--University of Pretoria, 2020. / UP Postgraduate Research Bursary / Computer Science / PhD / Unrestricted
8

System modeling with granular architectures of computational intelligence

Song, Mingli Unknown Date
No description available.
9

Energy Management for Automatic Monitoring Stations in Arctic Regions

Pimentel, Demian Unknown Date
No description available.
10

Niching ant colony optimisation

Angus, Daniel John. January 2008 (has links)
Thesis (Ph.D) - Swinburne University of Technology, Faculty of Information & Communication Technologies, Complex Intelligent Systems Laboratory, 2008. / Submitted in partial fulfilment for the degree of Doctor of Philosophy, Complex Intelligent Systems Laboratory, Faculty of Information and Communication Technologies, Swinburne University of Technology, 2008. Typescript. Includes bibliographical references (p. 169.181).

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