KEDS: a knowledge-based equation discovery system for engineering problems

2003 ◽  
Author(s):  
R.B. Rao ◽  
S.C.-Y. Lu
Keyword(s):  
Knowledge Based ◽  
Discovery System ◽  
Engineering Problems

A knowledge-based equation discovery system for engineering domains

IEEE Expert ◽  
1993 ◽  
Vol 8 (4) ◽  
pp. 37-42 ◽  
Author(s):  
R.B. Roa ◽  
S.C.-Y. Lu
Keyword(s):  
Knowledge Based ◽  
Discovery System

Integrating knowledge-based systems and artificial neural networks for engineering

1995 ◽  
Vol 9 (1) ◽  
pp. 13-22 ◽  
Author(s):  
Nabil Kartam ◽  
Ian Flood ◽  
Tanit Tongthong
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Generalized Solution ◽  
Knowledge Based Systems ◽  
Knowledge Based ◽  
Engineering Problems ◽  
Scope Of Application ◽  
Artificial Neural ◽  
The Individual

AbstractThe feasibility and relative merits of integrating knowledge-based systems (KBSs) and artificial neural networks (ANNs) for application to engineering problems are presented and evaluated. The strength of KBSs lies in their ability to represent human judgment and solve problems by providing explanations from and reasoning with heuristic knowledge. ANNs demonstrate problem solving characteristics not inherent in KBSs, including an ability to learn from example, develop a generalized solution applicable to a range of examples of the problem, and process information extremely rapidly. In this respect, KBSs and ANNs are complementary, rather than alternatives, and may be integrated into a system that exploits the advantages of both technologies. The scope of application and quality of solutions produced by such a hybrid extend beyond the boundaries of the individual technologies. This paper identifies and describes how KBSs and ANNs can be integrated, and provides an evaluation of the advantages that will accrue in engineering applications.

USING KNOWLEDGE-BASED SYSTEMS WITH HIERARCHICAL ARCHITECTURES TO GUIDE EVOLUTIONARY SEARCH

2000 ◽  
Vol 09 (01) ◽  
pp. 27-44 ◽  
Author(s):  
XIDONG JIN ◽  
ROBERT G. REYNOLDS
Keyword(s):  
Optimization Problems ◽  
Hierarchical Architecture ◽  
Evolutionary Search ◽  
Cultural Algorithm ◽  
Evolutionary System ◽  
Knowledge Based ◽  
Engineering Problems ◽  
Hard Problems ◽  
Hierarchical Architectures ◽  
Optimization Search

Regional Knowledge is useful in identifying patterns of relationships between variables, and it is particularly important in solving constrained global optimization problems. However, regional knowledge is generally unavailable prior to the optimization search. The questions here are: 1) Is it possible for an evolutionary system to learn regional knowledge during the search instead of having to acquire it beforehand? and 2) How can this regional knowledge be used to expedite evolutionary search? This paper defines regional schemata to provide an explicit mechanism to support the acquisition, storage and manipulation of regional knowledge. In a Cultural Algorithm framework, the belief space "contains" a set of these regional schemata, arranged in a hierarchical architecture, to enable the knowledge-based evolutionary system to learn regional knowledge during the search and apply the learned knowledge to guide the search. This mechanism can be used to guide the optimization search in a direct way, by "pruning" the infeasible regions and "promoting" the promising regions. Engineering problems with nonlinear constraints are tested and the results are discussed. It shows that the proposed mechanism is potential to solve complicated non-linear constrained optimization problems, and some other hard problems, e.g. the optimization problems with "ridges" in landscapes.

AI-DEQSOL. A knowledge-based environment for numerical simulation of engineering problems described by partial differential equations

1992 ◽  
Vol 6 (3) ◽  
pp. 199-212 ◽  
Author(s):  
D. P. Finn ◽  
N. J. Hurley ◽  
N. Sagawa
Keyword(s):  
Numerical Simulation ◽  
Partial Differential Equations ◽  
Problem Solving ◽  
Differential Equations ◽  
Knowledge Bases ◽  
Knowledge Based ◽  
Problem Solving Environment ◽  
Engineering Problems ◽  
User Groups ◽  
Modelling Environment

This paper presents a knowledge-based problem solving environment for numerical simulation of problems described by partial differential equations (PDEs). The system aims to facilitate the simulation requirements of different user groups that include engineers, mathematicians and numerical analysts. To attain this objective, a flexible multi-perspective modelling environment is proposed which incorporates three natural modelling platforms, namely; a physical model, a mathematical model and a numerical model. The modelling environment is integrated with a sophisticated numerical solver. We believe that combination of an open modelling system with a basic numerical simulator provides a powerful problem solving environment capable of meeting the needs of these different user groups. The overall system architecture is based on automatic transformation using mathematical and numerical knowledge bases between the three identified models. The knowledge bases are organized in a frame based manner to reflect the hierarchical nature of the knowledge in PDEs and numerical algorithms. The object oriented paradigm is used to bind local rule bases to each frame and for implementing a global inference mechanism which works over the hierarchical knowledge structures. Evaluation of the modelling environment has indicated that engineers can tackle PDE based engineering problems without the necessity for detailed knowledge of mathematics or numerical techniques and mathematicians can examine the mathematical properties of PDEs without the requirement of numerical expertise.

Is the Personal Always Political?

2017 ◽  
Vol 38 (3) ◽  
pp. 133-143 ◽  
Author(s):  
Danny Osborne ◽  
Yannick Dufresne ◽  
Gregory Eady ◽  
Jennifer Lees-Marshment ◽  
Cliff van der Linden
Keyword(s):  
Survey Data ◽  
National Survey ◽  
Negative Relationship ◽  
Openness To Experience ◽  
Political Sophistication ◽  
Knowledge Based

Abstract. Research demonstrates that the negative relationship between Openness to Experience and conservatism is heightened among the informed. We extend this literature using national survey data (Study 1; N = 13,203) and data from students (Study 2; N = 311). As predicted, education – a correlate of political sophistication – strengthened the negative relationship between Openness and conservatism (Study 1). Study 2 employed a knowledge-based measure of political sophistication to show that the Openness × Political Sophistication interaction was restricted to the Openness aspect of Openness. These studies demonstrate that knowledge helps people align their ideology with their personality, but that the Openness × Political Sophistication interaction is specific to one aspect of Openness – nuances that are overlooked in the literature.

Connectives Increase Knowledge-Based Inferences

1994 ◽  
Author(s):  
Gregory Barker ◽  
Keith Millis ◽  
Jonathan M. Golding
Keyword(s):  
Knowledge Based

The contribution of low-level sensory features versus knowledge-based factors for encoding of objects in working memory

2013 ◽  
Author(s):  
Valerio Santangelo ◽  
Simona Arianna Di Francesco ◽  
Serena Mastroberardino ◽  
Emiliano Macaluso
Keyword(s):  
Working Memory ◽  
Low Level ◽  
Knowledge Based ◽  
Sensory Features
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