Hybrid Neural Network and Expert Systems

Medsker, Larry R.

doi:10.1007/978-1-4615-2726-8

Cited by 139 publications

(60 citation statements)

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“…A recently announced volume, Medsker [31], is devoted to hybrid neural and symbolic expert systems; the present authors have not had the opportunity to see it. The table of contents, as announced, features in Ch.…”

Section: Symbolic and Neural Integration: A Comparisonmentioning

confidence: 99%

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

Nissan

Siegelmann

Galperin

et al. 1997

Engineering with Computers

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Section: Symbolic and Neural Integration: A Comparisonmentioning

confidence: 99%

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

Nissan

Siegelmann

Galperin

et al. 1997

Engineering with Computers

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“…Hybrid advisory system model From the point of view of reciprocal unification of intelligent techniques (fuzzy sets, artificial neural networks) in one intelligent tool -advisory system, system's character may be described as hybrid. Examples of other hybrid models one can find in [3][4][5].…”

Section: Advisory System Ideamentioning

confidence: 99%

Hybrid Advisory Systems and the Possibilities of IT Usage in the Process of Industrial Flooring Repairs

Gajzler¹

2008

ISARC 2008 - Proceedings From the 25th International Symposium on Automation and Robotics in Construction

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It is very often the case that the units responsible for the maintenance of the building have to repair industrial flooring. The large number of available materials and technologies as well as some other factors, make it necessary to use tools which would help to make good decisions. Hybrid advisory system is a tool which supports the process of decision making and is responsible for generating possible solutions and related technological and economical information. From the point of view of the decision-maker such information is necessary in order to make the right choices. The system generates answers based on the input vector which accounts for factors such as the type of the damage, its size, the time during which the floor was affected by various influences and the possibilities of reparation. Emphasized hybridity refers to the fact that within one system there are numerous intelligent tools connected to the individual system modules. There are also some aspects of "methodological" hybridity, which is represented by the two ways of approaching the methods of knowledge acquisition in the system.

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“…That is, there have only recently been books published on hybrid NN-supported expert systems (Medsker, 1994). Such hybrids tend to use experts' rules when possible, but they also use NNs to try to learn patterns, on which to base advice, in those parts of the problem where complexity overwhelms even the expert.…”

Section: Euphoria and Disillusionmentioning

confidence: 99%

Guest Editorial

Wyatt¹

1996

Environ Plann B Plann Des

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Transcending the retreat from rationalityOne of the aims of this issue is to provide something for planning practitioners. Unlike their academic cousins, planning practitioners have seldom had the luxury of 'retreating from rationality'. They have always had a job to do, rationally; in this job they were once comprehensively assisted by researchers into computer-aided planning techniques. Yet the intensity of academia's retreat from rationality and the popularity of its rejection of 'rational comprehensiveness' have tended to inhibit such assistance for at least two decades.Recently, however, there have been signs of a regrouping. New forms of 'organic' computing have emerged and their application to planning problems has been widespread. They therefore have the potential to restore academia's technical assistance back to something like the levels enjoyed by practitioners during the 1950s and 1960s. Accordingly, although it hardly proclaims that a second quantitative revolution is almost at hand (Openshaw, 1993), this special issue attempts to present, clearly, some cutting-edge techniques which have great promise.The techniques of interest can be loosely grouped under the umbrella term of 'connectionist' computing techniques. Each focuses on connections within data. They include simulated neural networks, which 'learn' to connect inputs with their associated outputs, and genetic algorithms, which connect parts of some problem solutions to parts of other problem solutions in order to 'breed' improved solutions. They also include cellular automata, which connect the simple behaviours of individual elements in order to generate complex models of the overall system.To appreciate fully the true potential of these techniques, it is necessary to understand how their planning applications evolved. And all applications were able to evolve only because of at least four decades of progress within three separate disciplines-computer-aided planning, artificial intelligence, and connectionist computing itself. Each of these fields passed through similar phases of euphoria in the 1950s and 1960s, disillusion during the 1970s and 1980s, and revival throughout the 1990s. But there were differences in the depths of unpopularity to which their techniques sank during the dark days, there were differences in their levels of 'born again' enthusiasm during the 1990s, and so today there are differences in the nature and potential of their respective contributions.Accordingly, in this editorial essay I will trace the evolution of each field. The conclusion is that connectionist-type computer-aided planning is far less ambitious, and more modest, than the modelling which occurred during the heady days of the 1960s.But I will also argue that the chances of a genuinely productive partnership between humanist and boffin are far greater this time around. Moreover, I will suggest there are signs that computer-based comprehensive contributions to planning, as distinct from the segmented and restricted contributions of today, may one day becom...

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Hybrid Neural Network and Expert Systems

Cited by 139 publications

References 0 publications

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

Upgrading automation for nuclear fuel in-core management: From the symbolic generation of configurations, to the neural adaptation of heuristics

Hybrid Advisory Systems and the Possibilities of IT Usage in the Process of Industrial Flooring Repairs

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