Near-term applications of knowledge based systems to combat helicopters

1990 ◽  
Author(s):  
FREDERICK HUBER ◽  
FRANK KILMER
Keyword(s):  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Near Term

Book review: Introduction to Knowledge Based Systems

IEE Review ◽  
1988 ◽  
Vol 34 (1) ◽  
pp. 36
Author(s):  
S.H. Lavington
Keyword(s):  
Knowledge Based Systems ◽  
Knowledge Based

Survey of the State-of-the-Art Expert/Knowledge Based Systems in Civil Engineering.

1986 ◽  
Author(s):  
Simon S. Kim ◽  
Mary Lou Maher ◽  
Raymond E. Levitt ◽  
Martin F. Rooney ◽  
Thomas J. Siller
Keyword(s):  
Civil Engineering ◽  
Expert Knowledge ◽  
State Of The Art ◽  
The State ◽  
Knowledge Based Systems ◽  
Knowledge Based

Knowledge-Based Systems Research

1990 ◽  
Author(s):  
Edward A. Feigenbaum ◽  
Robert S. Engelmore ◽  
Paul C. Rosenbloom
Keyword(s):  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Systems Research

A blackboard / knowledge - based systems approach to manufacturing scheduling and control

1990 ◽  
Vol 19 (1-4) ◽  
pp. 72-76
Author(s):  
C. Parks ◽  
J. Subramanian ◽  
S. Srinivas ◽  
A. Waikar ◽  
G. Graves ◽  
...  
Keyword(s):  
Systems Approach ◽  
Knowledge Based Systems ◽  
Manufacturing Scheduling ◽  
Knowledge Based ◽  
And Control

Adaptive interaction with knowledge-based systems

1994 ◽  
Author(s):  
F. de Rosis ◽  
M. T. Cozza ◽  
B. de Carolis ◽  
S. Errore ◽  
S. Pizzutilo ◽  
...  
Keyword(s):  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Adaptive Interaction

Optimization of Preloaded Coefficient Based on the Adapted CBR

2009 ◽  
Vol 628-629 ◽  
pp. 13-18
Author(s):  
H.L. Li ◽  
Li Hui Lang ◽  
W. Jiao ◽  
H.Z. Su
Keyword(s):  
Genetic Algorithm ◽  
Optimum Design ◽  
Prediction Error ◽  
Selection Model ◽  
Knowledge Based Systems ◽  
Estimation Model ◽  
Knowledge Based ◽  
Adjustment Model ◽  
Successful Technique

Selecting an appropriate preloaded coefficient has always been a challenge in wire- winding prestressed structure optimum design. Cased-based reasoning (CBR) has become a successful technique for knowledge-based systems in many domains. However, hardly any research has addressed the issue of how to generate the adaptation solution when the case has been retrieved. The present paper investigates the adoption of genetic algorithm(GA) to explore the suitable adjustment model. Two adapted model were presented and assessed in terms of their mean relative prediction error rates.The experiment results shown that applying GA to adjust the preloaded coefficient selection model is a feasible approach to largely improve the accuracy of estimation model. It also demonstrate that the adapted CBR presents better estimate accuracy than the results ontained by other unadapted CBR methods.

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