Mark A. Kramer

发表

A robust event-oriented methodology for diagnosis of dynamic process systems

F. E. Finch, O. O. Oyeleye, Mark A. Kramer, 1990 .

A rule‐based approach to fault diagnosis using the signed directed graph

Mark A. Kramer, B. L. Palowitch, M. Kramer, 1987 .

A NEURAL NETWORK ARCHITECTURE THAT COMPUTES ITS OWN RELIABILITY

Lyle H. Ungar, Mark A. Kramer, J. A. Leonard, 1992 .

Diagnosis using backpropagation neural networks—analysis and criticism

Mark A. Kramer, J. A. Leonard, M. Kramer, 1990 .

The Application of a Knowledge-Based Expert System to Chemical Plant Fault Diagnosis

Mark A. Kramer, Bernard L. Palowitch, 1985, 1985 American Control Conference.

Modeling chemical processes using prior knowledge and neural networks

Mark A. Kramer, Michael L. Thompson, M. Kramer, 1994 .

Fault detection and diagnosis using neural network approaches

Mark A. Kramer, M. Kramer, 1992 .

PSF Boosting Of Multi-Path, Phase-Conjugate Return By Compensating For Depolarization

Mark A. Kramer, Andrew T. Klemas, 1988, Photonics West - Lasers and Applications in Science and Engineering.

Narrowing diagnostic focus using functional decomposition

F. E. Finch, Mark A. Kramer, 1988 .

GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain Systems Modeled with Bayesian Belief Networks

Mark A. Kramer, Carlos Rojas-Guzmán, M. Kramer, 1993, UAI.

A hybrid modeling methodology to combine prior knowledge and neural networks

Mark A. Kramer, Michael L. Thompson, M. L. Thompson, 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

Parametric sensitivity analysis of complex process flowsheets using sequential modular simulation

Mark A. Kramer, J. R. Leis, A. Gallagher, 1987 .

Improvement of the backpropagation algorithm for training neural networks

Mark A. Kramer, J. A. Leonard, M. Kramer, 1990 .

Airborne laser advanced concepts testbed

Mark A. Kramer, Todd D. Steiner, Robert Russell Butts, 1998, Defense, Security, and Sensing.

A general framework for preventive maintenance optimization in chemical process operations

Mark A. Kramer, J. S. Tan, Jonathan Samuel Tan, 1997 .

Development and classification of expert systems for chemical process fault diagnosis

F. E. Finch, Mark A. Kramer, F. Eric Finch, 1988 .

Autoassociative neural networks

Mark A. Kramer, M. Kramer, 1992 .

Malfunction diagnosis using quantitative models with non‐boolean reasoning in expert systems

Mark A. Kramer, 1987 .

Qualitative modeling and fault diagnosis of dynamic processes by MIDAS

F. E. Finch, Mark A. Kramer, O. O. Oyeleye, 1992 .

Classifying Process Behavior with Neural Networks: Strategies for Improved Training and Generalization

Mark A. Kramer, James A. Leonard, 1990, 1990 American Control Conference.

An Effective Robust Method for Removing Measurement Errors from Plant Data

Mark A. Kramer, Lloyd P. M. Johnston, 1996 .

Fault Diagnosis of Chemical Processes

F. E. Finch, Mark A. Kramer, F. Eric Finch, 1989 .

An evolutionary programming approach to probabilistic model-based fault diagnosis of chemical processes

Mark A. Kramer, Carlos Rojas-Guzman, M. Kramer, 1995 .

A reliable neural network architecture for fault diagnosis and modelling of chemical processes

Mark A. Kramer, J. A. Leonard, M. Kramer, 1992 .

An Evolutionary Computing Approach to Probabilistic Reasoning on Bayesian Networks

Mark A. Kramer, Carlos Rojas-Guzmán, M. Kramer, 1996, Evolutionary Computation.

Hazard function modeling using cross validation: From data collection to model selection

Mark A. Kramer, Jonathan S. Tan, 1995 .

Automated diagnosis of malfunctions based on object orientated programming

Mark A. Kramer, 1988 .

Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes

Mark A. Kramer, Carlos Rojas-Guzman, M. Kramer, 1993 .