&lt;title&gt;Comparison of polynomial network and model-based target recognition&lt;/title&gt;

Drake, Keith C.; Kim, Richard; Kim, Tony Y.; Johnson, Owen D.

doi:10.1117/12.179030

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…NPN software programs based on GMDH-type algorithms have been described using various terms, including polynomial network (Barron et al, 1984;Drake et al, 1994;Griffin et al, 1994;Kleinsteuber and Sepehri, 1996), abductory induction (Montgomery, 1989), abductive reasoning network (Montgomery and Drake, 1991), and abductive polynomial network (Drake and Kim, 1997). More recently, polynomial network software programs have been classed as data mining tools (Agarwal, 1999;Kim, 2002;King et al, 1998;and Pyo et al, 2002).…”

Section: Nonlinear Polynomial Network Modelingmentioning

confidence: 99%

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Watson¹,

Bostic²,

Harrison³

2009

Transactions of the ASABE

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Linear multiple regression (LMR) and nonlinear polynomial network (NPN) models were developed from data collected from ISO 8178-4 (1996) test cycle B-type tests (ISO) and an expanded set of tests (EXP) to predict hydrocarbon (HC) emissions from a diesel engine. LMR using the ISO training data (R 2 = 0.94) resulted in overfitting of the model as applied to the evaluation data (R 2 = 0.49). LMR based on the expanded data (R 2 = 0.68) was a better LMR model when applied to the evaluation data (R 2 = 0.64). NPN using the expanded training data (R 2 = 0.99) resulted in the best model when applied to the evaluation data (R 2 = 0.98) and is preferred for predicting HC when the larger set of test mode data are available. NPN using the ISO training data (R 2 = 0.99) resulted in a satisfactory fit for the evaluation data (R 2 = 0.91), although with a higher average absolute error (0.52 vs. 0.42 g/kWh) than NPN using the EXP training data. This model was also considered suitable for predicting HC. Results of this initial study suggest that data could be collected during ISO 8178-4 emission tests and modeled with NPN to predict HC emissions for a diesel engine operating at various constant speeds and loads.

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Section: Nonlinear Polynomial Network Modelingmentioning

confidence: 99%

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Watson¹,

Bostic²,

Harrison³

2009

Transactions of the ASABE

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“…r Target classification Chen and Walton (1986). apply nearest-neighbour methods to ship and aircraft classification using radar cross-section measurements Drake et al (1994). compare several methods, including k nearest neighbour on multispectral imagery.r Handwritten character recognition.…”

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confidence: 99%

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2002

Statistical Pattern Recognition

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The website www.wiley.com/go/statistical_pattern_recognition contains supplementary material on topics including measures of dissimilarity, estimation, linear algebra, data analysis and basic probability.xxii PREFACE who have provided encouragement and made comments on various parts of the manuscript. In particular, we would like to thank Anna Skeoch for providing figures for Chapter 12; and Richard Davies and colleagues at John Wiley for help in the final production of the manuscript. Andrew Webb is especially thankful to Rosemary for her love, support and patience.

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Hierarchical integration of sensor data and contextual information for automatic target recognition

Drake

Kim

1995

Appl Intell

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<title>Comparison of polynomial network and model-based target recognition</title>

Cited by 4 publications

References 8 publications

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Comparative Models of Hydrocarbon Emissions for a Diesel Engine Operating at Constant Loads and Speeds

Additional Topics

Hierarchical integration of sensor data and contextual information for automatic target recognition

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