Engineering Commons^™

A Training Sample Sequence Planning Method For Pattern Recognition Problems, Chen-Wen Yen, Chieh-Neng Young, Mark L. Nagurka

Mechanical Engineering Faculty Research and Publications

In solving pattern recognition problems, many classification methods, such as the nearest-neighbor (NN) rule, need to determine prototypes from a training set. To improve the performance of these classifiers in finding an efficient set of prototypes, this paper introduces a training sample sequence planning method. In particular, by estimating the relative nearness of the training samples to the decision boundary, the approach proposed here incrementally increases the number of prototypes until the desired classification accuracy has been reached. This approach has been tested with a NN classification method and a neural network training approach. Studies based on both artificial and …

Study And Design Of An Intelligent Preconditioner Recommendation System, Shuting Xu

University of Kentucky Doctoral Dissertations

There are many scientific applications in which there is a need to solve very large linear systems. The preconditioned Krylove subspace methods are considered the preferred methods in this field. The preconditioners employed in the preconditioned iterative solvers usually determine the overall convergence rate. However, choosing a good preconditioner for a specific sparse linear system arising from a particular application is the combination of art and science, and presents a formidable challenge for many design engineers and application scientists who do not have much knowledge of preconditioned iterative methods.

We tackled the problem of choosing suitable preconditioners for particular applications …

Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives …

Taxonomy Of Systems-Of-Systems, James Gideon, Cihan H. Dagli, Ann K. Miller

Engineering Management and Systems Engineering Faculty Research & Creative Works

The study of systems-of-systems is an increasingly important topic in systems engineering. Though there is not complete agreement, a more precise definition of what these highly evolved systems are and what attributes they possess has certainly emerged. However, there are still areas in the study where the topic can be advanced by a more rigorous presentation of the basic elements. One such area is the taxonomy of systems-ofsystems. This paper will begin with the definition of systems-of-systems as it currently stands and will present the taxonomy from a broader view with additional considerations for classification. These taxonomic categories will consider …