Titlebook: Hybrid Neural Network and Expert Systems; Larry R. Medsker Book 1994 Springer Science+Business Media New York 1994 artificial intelligence

虛弱

978-1-4613-6175-6Springer Science+Business Media New York 1994

令人作嘔

易發(fā)怒

Overview of Neural and Symbolic Systemsome cases, neural computing systems are replacing expert systems and other AI solutions. In other applications, neural networks provide features not possible with conventional AI systems, and they could provide aspects of intelligent behavior that have thus far eluded the AI symbolic/logical approach.

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LAMtm Hybrid System for Window Glazing Designineer with E. I. du Pont de Nemours & Company, Du Pont Polymers and Automotive - “Butacite”, Parkersburg, W.V. Dieter H. Droste also collaborated in this project. Mr. Foss is the primary author of this chapter.

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Chemical Tank Control Systemere an example using a system that controls the pH value of a controlled stirred chemical tank reactor. The system was created using the Conncert hybrid neural network/expert system development environment described in Chapter 2 and by Wilson and Hendler (1993).

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Hybrid System for Multiple Target Recognitionk/expert system approach to multiple target recognition based on their in-house hybrid development tool NueX. The work was sponsored by the Naval Surface Warfare Center, Dahlgren Division, under contract No. N60921-91-C0186.

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Guidelines for Developing Hybrid Systemsecific facts with information from clients or users about specific instances of problems in the knowledge domains of the expert systems. Ideally, reasoning can be explained and the knowledge bases easily modified, independent of the inference engine, as new rules become know.

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Image Interpretation Via Fusion of Heterogeneous Sources Using a Hybrid Expert-Neural Network SystemThis chapter was written by Naresh Gupta, Srinivasan Raghavan, Gretchen Bailey, and Laveen Kanal at the LNK Corporation, Inc. They describe the system InFuse, which addresses the multi-source integration problem.