Isbn: 9780792383802 - Traffic Control and Transport Planning:: A Fuzzy Sets and Neural Networks Approach: 13 (international Series in Intelligent Technologies, 13) (14 results)

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Condition: Used. pp. 412.

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Condition: New. Acquaints the reader with the basic elements of fuzzy set theory, fuzzy logic, fuzzy logic systems, artificial neural networks, neurofuzzy modeling, and applications of fuzzy logic and neural networks to date in traffic and transportation engineering. Series: International Series in Intelligent Technologies. Num Pages: 387 pages, biography. BIC Classification: KNG; PBWX. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 23. Weight in Grams: 752. . 1998. Hardback. . . . .…

Condition: Used. pp. 412 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using 'classical' mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made by the analyst when forming the mathematical model, the linguistic information is often ignored. On the other hand, a wide range of traffic and transportation engineering parameters are characterized by uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective knowledge or linguistic information on a daily basis when making decisions. Decisions about route choice, mode of transportation, most suitable departure time, or dispatching trucks are made by drivers, passengers, or dispatchers. In each case the decision maker is a human. The environment in which a human expert (human controller) makes decisions is most often complex, making it difficult to formulate a suitable mathematical model. Thus, the development of fuzzy logic systems seems justified in such situations. In certain situations we accept linguistic information much more easily than numerical information. In the same vein, we are perfectly capable of accepting approximate numerical values and making decisions based on them. In a great number of cases we use approximate numerical values exclusively. It should be emphasized that the subjective estimates of different traffic parameters differs from dispatcher to dispatcher, driver to driver, and passenger to passenger.…

Condition: Used. pp. 412.

Condition: New. Acquaints the reader with the basic elements of fuzzy set theory, fuzzy logic, fuzzy logic systems, artificial neural networks, neurofuzzy modeling, and applications of fuzzy logic and neural networks to date in traffic and transportation engineering. Series: International Series in Intelligent Technologies. Num Pages: 387 pages, biography. BIC Classification: KNG; PBWX. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 23. Weight in Grams: 752. . 1998. Hardback. . . . . Books ship from the US and Ireland.…

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using classical mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made.…

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using 'classical' mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made by the analyst when forming the mathematical model, the linguistic information is often ignored. On the other hand, a wide range of traffic and transportation engineering parameters are characterized by uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective knowledge or linguistic information on a daily basis when making decisions. Decisions about route choice, mode of transportation, most suitable departure time, or dispatching trucks are made by drivers, passengers, or dispatchers. In each case the decision maker is a human. The environment in which a human expert (human controller) makes decisions is most often complex, making it difficult to formulate a suitable mathematical model. Thus, the development of fuzzy logic systems seems justified in such situations. In certain situations we accept linguistic information much more easily than numerical information. In the same vein, we are perfectly capable of accepting approximate numerical values and making decisions based on them. In a great number of cases we use approximate numerical values exclusively. It should be emphasized that the subjective estimates of different traffic parameters differs from dispatcher to dispatcher, driver to driver, and passenger to passenger.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 412 pp. Englisch.…

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -When solving real-life engineering problems, linguistic information is often encountered that is frequently hard to quantify using 'classical' mathematical techniques. This linguistic information represents subjective knowledge. Through the assumptions made by the analyst when forming the mathematical model, the linguistic information is often ignored. On the other hand, a wide range of traffic and transportation engineering parameters are characterized by uncertainty, subjectivity, imprecision, and ambiguity. Human operators, dispatchers, drivers, and passengers use this subjective knowledge or linguistic information on a daily basis when making decisions. Decisions about route choice, mode of transportation, most suitable departure time, or dispatching trucks are made by drivers, passengers, or dispatchers. In each case the decision maker is a human. The environment in which a human expert (human controller) makes decisions is most often complex, making it difficult to formulate a suitable mathematical model. Thus, the development of fuzzy logic systems seems justified in such situations. In certain situations we accept linguistic information much more easily than numerical information. In the same vein, we are perfectly capable of accepting approximate numerical values and making decisions based on them. In a great number of cases we use approximate numerical values exclusively. It should be emphasized that the subjective estimates of different traffic parameters differs from dispatcher to dispatcher, driver to driver, and passenger to passenger. 412 pp. Englisch.…