The KnowledgeBased Technology Applications Center (KBTAC) seminar series. Volume 4, Introduction to neural networks and fuzzy logic: Final report
Abstract
Neural Networks are adaptive systems that allow users to model complex systems, classify patterns, filter data or control processes with little or no apriori knowledge. Neural networks represent a branch of artificial intelligence technology. Fuzzy logic is a generalization of formalized mathematical logic that allows representation of uncertainty by providing a smooth transition from true to false, instead of a step change. It allows for a proposition to be both true and false, to different degrees at the same time. This allows representation of approximate reasoning concepts that occur frequently in everyday experience, such as ``somewhat true,`` or ``not very hot.`` To utilize these technologies, utility personnel need to acquire knowledge and skills in a number of areas: 1. basic principles of neural networks and fuzzy logic; 2. types of neural networks and fuzzy logic systems, their applications, and requirements for use; 3. considerations for designing neural networks and fuzzy logic systems; 4. software tools available for these technologies. Offtheshelf software is available for a variety of hardware platforms to rapidly develop, tune and test neural networks and fuzzy logic systems. Familiarity with the problem to which a neural network or fuzzy logic system is applied allows the designer tomore »
 Authors:

 Syracuse Univ., NY (United States). KnowledgeBased Technology Applications Center
 Publication Date:
 Research Org.:
 Electric Power Research Inst., Palo Alto, CA (United States); Syracuse Univ., NY (United States). KnowledgeBased Technology Applications Center
 Sponsoring Org.:
 Electric Power Research Inst., Palo Alto, CA (United States)
 OSTI Identifier:
 10130980
 Report Number(s):
 EPRITR101740V4
ON: UN94007715
 Resource Type:
 Technical Report
 Resource Relation:
 Other Information: PBD: Dec 1993
 Country of Publication:
 United States
 Language:
 English
 Subject:
 22 GENERAL STUDIES OF NUCLEAR REACTORS; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; NEURAL NETWORKS; FUZZY LOGIC; POWER SYSTEMS; REACTOR CONTROL SYSTEMS; EXPERT SYSTEMS; ARTIFICIAL INTELLIGENCE; KNOWLEDGE BASE; TECHNOLOGY TRANSFER; MANMACHINE SYSTEMS; 220400; 990200; CONTROL SYSTEMS; MATHEMATICS AND COMPUTERS
Citation Formats
Isik, C, and Wood, R M. The KnowledgeBased Technology Applications Center (KBTAC) seminar series. Volume 4, Introduction to neural networks and fuzzy logic: Final report. United States: N. p., 1993.
Web.
Isik, C, & Wood, R M. The KnowledgeBased Technology Applications Center (KBTAC) seminar series. Volume 4, Introduction to neural networks and fuzzy logic: Final report. United States.
Isik, C, and Wood, R M. Wed .
"The KnowledgeBased Technology Applications Center (KBTAC) seminar series. Volume 4, Introduction to neural networks and fuzzy logic: Final report". United States.
@article{osti_10130980,
title = {The KnowledgeBased Technology Applications Center (KBTAC) seminar series. Volume 4, Introduction to neural networks and fuzzy logic: Final report},
author = {Isik, C and Wood, R M},
abstractNote = {Neural Networks are adaptive systems that allow users to model complex systems, classify patterns, filter data or control processes with little or no apriori knowledge. Neural networks represent a branch of artificial intelligence technology. Fuzzy logic is a generalization of formalized mathematical logic that allows representation of uncertainty by providing a smooth transition from true to false, instead of a step change. It allows for a proposition to be both true and false, to different degrees at the same time. This allows representation of approximate reasoning concepts that occur frequently in everyday experience, such as ``somewhat true,`` or ``not very hot.`` To utilize these technologies, utility personnel need to acquire knowledge and skills in a number of areas: 1. basic principles of neural networks and fuzzy logic; 2. types of neural networks and fuzzy logic systems, their applications, and requirements for use; 3. considerations for designing neural networks and fuzzy logic systems; 4. software tools available for these technologies. Offtheshelf software is available for a variety of hardware platforms to rapidly develop, tune and test neural networks and fuzzy logic systems. Familiarity with the problem to which a neural network or fuzzy logic system is applied allows the designer to develop an appropriate design. Therefore, it is desirable to teach neural network and fuzzy logic technology to utility experts in the domain of application.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1993},
month = {12}
}