Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Computer Sciences (2)
- Databases and Information Systems (2)
- Education (2)
- Higher Education (2)
- OS and Networks (2)
-
- Statistics and Probability (2)
- Agribusiness (1)
- Agricultural Economics (1)
- Agricultural Science (1)
- Agriculture (1)
- Agronomy and Crop Sciences (1)
- Applied Statistics (1)
- Business (1)
- Environmental Monitoring (1)
- Environmental Sciences (1)
- Life Sciences (1)
- Natural Resource Economics (1)
- Natural Resources Management and Policy (1)
- Plant Breeding and Genetics (1)
- Plant Sciences (1)
- Statistical Models (1)
- Institution
- Keyword
-
- Communications (2)
- Computer science (2)
- Data (2)
- Databases (2)
- Educational technology (2)
-
- Higher education (2)
- Programming (2)
- Technology (2)
- Bayesian statistical decision theory (1)
- Dryland farming (1)
- Farm wheat supply (1)
- Forecasting--Data processing (1)
- Global Positioning System (1)
- Inertial navigation systems (1)
- Kalman filtering (1)
- Knowledge acquisition (Expert systems) (1)
- MUDAS (Model of an Uncertain Dryland Agricultural System) (1)
- Neural networks (Computer science) (1)
- Reasoning--Data processing (1)
- Risk adverse (1)
- Risk management (1)
- Soils (1)
- Time-series analysis--Data processing (1)
- Publication
- Publication Type
Articles 1 - 6 of 6
Full-Text Articles in Data Science
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
Theses and Dissertations
An extended Kalman filter (EKE) is used to combine the information obtained from a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) to provide a navigation solution. This research compares the results of a tightly-coupled GPS/INS integrated system with a loosely-coupled integrated system, using real world data. A fair comparison is accomplished by using the same sets of data, and keeping the integration structures as close as possible. Both integrations are feedforward and have the same error states in the navigation Kalman filters. Differences between the two, such as navigation solutions and tuning values, are shown in …
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
Theses and Dissertations
This thesis uses multiple layer perceptrons (MLP) neural networks and Kohonen clustering networks to predict and assign confidence to nonlinear time series classifications. The nonlinear time series used for analysis is the Standard and Poor's 100 (S&P 100) index. The target prediction is classification of the daily index change. Financial indicators were evaluated to determine the most useful combination of features for input into the networks. After evaluation it was determined that net changes in the index over time and three short-term indicators result in better accuracy. A back-propagation trained MLP neural network was then trained with these features to …
Scis Networking - July 1995, Nova Southeastern University
Scis Networking - July 1995, Nova Southeastern University
CCIS Networking / SCIS Networking magazines
No abstract provided.
Effects Of Tactical Responses And Risk Aversion On Farm Wheat Supply, Ross S. Kingwell
Effects Of Tactical Responses And Risk Aversion On Farm Wheat Supply, Ross S. Kingwell
Natural Resources Research Articles
A discrete stochastic programming model of the farming system of the eastern wheatbelt of Western Australia is used to examine the effect of tactical responses and risk aversion on wheat supply. Including within-season tactical changes to wheat areas decreases the own-price elasticity of supply. By contrast, introducing risk aversion has no consistent effect on the own-price elasticity of supply. The implications for supply models are discussed.
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Theses and Dissertations
This thesis develops a methodology and a tool for knowledge acquisition with the new probabilistic knowledge representation-the Bayesian Forest. It establishes the structure of the Knowledge Acquisition and Maintenance module of the Probabilities. Expert Systems, Knowledge and Inference (PESKI) architecture. The tool, MACK, is designed to be used directly by the domain expert(s) rather than by knowledge engineer(s), and thus supports automated knowledge acquisition. This research determines and implements the constraints necessary to ensure the consistency of Bayesian Forest knowledge bases as data is both acquired and subsequently maintained. The impact to the PESKI architecture of time-dependent information and default …
Scis Networking - January 1995, Nova Southeastern University - Shepard Broad College Of Law
Scis Networking - January 1995, Nova Southeastern University - Shepard Broad College Of Law
CCIS Networking / SCIS Networking magazines
No abstract provided.