Integration of a knowledge-based system, artificial neural networks and multimedia for gear design

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.

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Small-signal and noise modeling of class of HEMTs using knowledge-based artificial neural networks

International Journal of RF and Microwave Computer-Aided Engineering ◽

10.1002/mmce.20631 ◽

2012 ◽

Vol 23 (1) ◽

pp. 34-39 ◽

Cited By ~ 13

Author(s):

Zlatica Marinković ◽

Olivera Pronić-Rančić ◽

Vera Marković

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Small Signal ◽

Noise Modeling ◽

Knowledge Based ◽

Artificial Neural

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VISUALIZING LEARNING AND COMPUTATION IN ARTIFICIAL NEURAL NETWORKS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213092000260 ◽

1992 ◽

Vol 01 (03) ◽

pp. 399-425 ◽

Cited By ~ 36

Author(s):

MARK W. CRAVEN ◽

JUDE W. SHAVLIK

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Inductive Learning ◽

Numerical Data ◽

Initial Weight ◽

General Technique ◽

Knowledge Based ◽

Decision Making Processes ◽

Artificial Neural ◽

Visualization Techniques

Scientific visualization is the process of using graphical images to form succinct and lucid representations of numerical data. Visualization has proven to be a useful method for understanding both learning and computation in artificial neural networks. While providing a powerful and general technique for inductive learning, artificial neural networks are difficult to comprehend because they form representations that are encoded by a large number of real-valued parameters. By viewing these parameters pictorially, a better understanding can be gained of how a network maps inputs into outputs. In this article, we survey a number of visualization techniques for understanding the learning and decision-making processes of neural networks. We also describe our work in knowledge-based neural networks and the visualization techniques we have used to understand these networks. In a knowledge-based neural network, the topology and initial weight values of the network are determined by an approximately-correct set of inference rules. Knowledge-based networks are easier to interpret than conventional networks because of the synergy between visualization methods and the relation of the networks to symbolic rules.

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