Discrete event fuzzy airport control

Clymer, John R.; Corey, Philip D.; Gardner, Judith A

doi:10.1109/21.148409

Cited by 26 publications

(5 citation statements)

References 17 publications

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“…In fuzzy rules, the strength of each rule measures the degree of belief that the rule covers an ambiguous case. The OpEM expert system controller can sort out fuzzy rules using adaptive rule strength modification [12]. Experiments with noisy features in the sonar system classification, shown in figure 17, confirm this.…”

Section: Ambiguous Casesmentioning

confidence: 50%

“…In addition, if each case is covered by a set of rules of increasing specificity, a default rule hierarchy is formed. The OpEM Adaptive Expert System Controller [7][8][10][11][12][13][14] has an adaptive mode of leaming, based on the &dquo;bucket brigade&dquo; algorithm [4,12,18], that automatically determines a rule 'strength', which is a weighting factor for each rule, that optimizes decision making. If a set of rules of increasing specificity is generated to cover each case, a default hierarchy of rules results.…”

Section: Theory Of Inductive Learningmentioning

confidence: 99%

“…An OpEM [7][8][9][10][11][12][13][14][15] simulation program was developed that simulates surface ships and submarines transiting a surveillance area. A collection of signals was associated with ship type, either surface ship or submarine, each having a particular speed.…”

Section: Introductionmentioning

confidence: 99%

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Induction Of Classification Rules From Noisy Sonar Features

1994

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Developing rules to classify surface ships and submarines based on noisy features extracted from underwater acoustic signals is difficult and time consuming; indeed, it is difficult to generate decision making rules for any complex system. The Operational Evaluation Modeling (OpEM) Simulation Tool Kit including Expert System Controller and Induction Program, discussed in this paper, greatly mitigates this problem for most complex system simulation projects such as airports, factories, or space defense systems. A theory for inductive learning of decision making rules in cybernetic systems is presented to provide a concise way-of thinking about inductive learning. An OpEM directed graph model is presented that describes operation of a sonar detection and classification system. An OpEM Pascal simulation of this system demonstrates how effective classification rules can be that are induced from simulation generated cases, even with noisy features present. The OpEM induction program is described and innovative features are discussed. Rules generated by the OpEM induction program are compared to rules obtained by Ross Quinlan's ID3 induction program. Performance of the OpEM induction program when noisy features are present is compared with ID3.

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Section: Ambiguous Casesmentioning

confidence: 50%

Section: Theory Of Inductive Learningmentioning

confidence: 99%

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Induction Of Classification Rules From Noisy Sonar Features

1994

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“…The Operational Evaluation Modeling (OpEM) graphical discrete event simulation language [12,13] was developed to describe the context-sensitive transitions required for CAS interactions. The challenge in developing the graphical version of this language was to apply lessons learned from experience obtained from the OpEM Pascal Simulation programming system [2,[12][13][14][15][16][17][18][19] used during the past 15 years. This system, though flexible in terms of data structures and event action code that can be created, is not easy to program.…”

Section: Discussionmentioning

confidence: 99%

Simulation-Based Engineering Of Complex Adaptive Systems

Clymer

1999

SIMULATION

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Systems to be developed in the future will likely be more complex than in the past. The number of behaviors a system can exhibit is one measure of system complexity. A complex system can be described as a large network of communicating subsystems. A regional or national Intelligent Transportation System exemplifies such a system. If each subsystem in the network adapts its behavior to collaborate with other subsystems to achieve overall goals, such a system is called a Complex Adaptive System (CAS). In this paper a building elevator system provides an example CAS in which each elevator collaborates with others to optimize system performance. A Context-Sensitive System model that explicitly deals with the complexity of CAS is dicussed. A graphical simulation language, Operational Evaluation Modeling for Context-Sensitive Systems (OpEMCSS), assists in understanding CAS and automates most complex simulation details and semantics. OpEMCSS is distinctive from most simulation languages that have a graphical user interface in its ability to duplicate processes without having to show every instance onscreen. OpEMCSS and its use in simulation-based systems engineering of CAS are discussed.

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“…This state-of-the-art graphical, discrete event simulation library works with ExtendTM (by Imagine That, Inc.) and has the power of much more costly, general-purpose simulation products. OpEMCSS, which was developed based on earlier work [5][6][7][8][9][10][11][12][13], is capable of representing several commonly used systems views including queuing, process and resource, and message flow models (see http://ecs.fullerton.edu/~jclymer~.…”

Section: Introduction Zmentioning

confidence: 99%

Optimization of Simulated System Effectiveness Using Evolutionary Algorithms

Clymer

1999

SIMULATION

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Using evolutionary algorithms, a search is performed based on a population where each population member consists of a vector of attribute values and a fitness value. A simulation of a system is run, given a particular set of the member attribute values, producing a fitness value. Fitness measures how well the system achieves its mission objectives. If the fitness has a random component, several runs are made to produce average fitness. The procedure is to select the best members from the population based on average fitness and mutate the member attribute values to produce new population members. Since population member attributes can affect process reaction times, wait logic, or decision logic, a search for the best attribute values over 50 to 100 generations can result in optimal fitness. In order to demonstrate the use of evolutionary algorithms in system optimization, a simple inventory system that has a complex fitness surface is considered.

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Discrete event fuzzy airport control

Cited by 26 publications

References 17 publications

Induction Of Classification Rules From Noisy Sonar Features

Induction Of Classification Rules From Noisy Sonar Features

Simulation-Based Engineering Of Complex Adaptive Systems

Optimization of Simulated System Effectiveness Using Evolutionary Algorithms

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