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[資源]
【分享】The Fuzzy Systems Handbook.Academic Press.1994
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The Fuzzy Systems Handbook_A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems 免責(zé)聲明 本資源來(lái)自于互聯(lián)網(wǎng)，僅供網(wǎng)絡(luò)測(cè)試之用，請(qǐng)務(wù)必在下載后24小時(shí)內(nèi)刪除！所有資源不涉及任何商業(yè)用途。發(fā)帖人不承擔(dān)由下載使用者引發(fā)的一切法律責(zé)任及連帶責(zé)任！ 著作權(quán)歸原作者或出版社所有。未經(jīng)發(fā)貼人conanwj許可，嚴(yán)禁任何人以任何形式轉(zhuǎn)貼本文，違者必究！ 如果本帖侵犯您的著作權(quán)，請(qǐng)與conanwj聯(lián)系，收到通知后我們將立即刪除此帖！ Authors(Editors): Earl Cox Publisher: Academic Press Pub Date:1994 Pages: 512 ISBN 0-12-194270-8 Preface While several books are available today that address the mathematical and philosophical foundations of fuzzy logic, none, unfortunately, provides the practicing knowledge engineer, system analyst, and project manager with specific, practical information about fuzzy system modeling. Those few books that include applications and case studies concentrate almost exclusively on engineering problems: pendulum balancing, truck backeruppers, cement kilns, antilock braking systems, image pattern recognition, and digital signal processing. Yet the application of fuzzy logic to engineering problems represents only a fraction of its real potential. As a method of encoding and using human knowledge in a form that is very close to the way experts think about difficult, complex problems, fuzzy systems provide the facilities necessary to break through the computational bottlenecks associated with traditional decision support and expert systems. Additionally, fuzzy systems provide a rich and robust method of building systems that include multiple conflicting, cooperating, and collaborating experts (a capability that generally eludes not only symbolic expert system users but analysts that have turned to such related technologies as neural networks and genetic algorithms). Yet the application of fuzzy logic in the areas of information technology, decision support, and database analysis and mining has been largely ignored by both the commercial vendors of decision support products and the knowledge engineers that use them. Fuzzy logic has not found its way into the information modeling field due to a number of factors that are rapidly changing—unfamiliarity with the concept, a predilection for the use of confidence factors and Bayesian probabilities among most knowledge engineers (stemming from the early successes of expert systems such as MYCIN, PROSPECTOR, and XCON), and a suspicion that there is something fundamentally wrong with a reasoning system that announces its own imprecision. Fuzzy logic is the essential oxymoron. Fuzzy logic, however, is a technology that has patiently bided its time. Today, in the world of highly complex, international business systems, webs of communications networks, high-density information overloads, and the recognition that many seemingly simple problems belie a deep nonlinearity, fuzzy logic is proving itself as a powerful tool in knowledge modeling. Fuzzy logic will soon usher in the second wave of intelligent systems. I have good reason to believe this prediction. A little more than 13 years ago, while marketing an enterprise modeling system in the United Kingdom, I was introduced to the idea of fuzzy logic by my friend Peter Llewelyn Jones. Peter is the author of REVEAL, the first commercial fuzzy expert system and, with Ian Graham, the author of Expert Systems: Knowledge, Uncertainty and Decision,1 one of the very first books on fuzzy information systems. Sitting one evening in a pub just off Fleet Street, and tucked neatly into the outskirts of Covent Gardens, about a block from our office on the Waterloo Bridge, Peter explained in clear and convincing terms just why fuzzy logic, in the more general form of approximate reasoning, was an important emerging technology. Like Paul on the road to Damascus, a brilliant light went off in my mind and I left the pub an eager devotee to the cult of fuzzy logic. Like all naive revolutionaries, we expected the world to welcome our insights and revealed truths with open arms. However, in spite of its evident potential and the success of many projects, REVEAL was shelved by its owners and fuzzy logic remained the arcane study of Lotfi Zadeh and his ever but slowly increasing circle of believers (usually graduate students who remained well within the sheltering walls of Evans Hall high on a hill at the University of California at Berkeley). In the years since I was introduced to and began using fuzzy logic, I have seen firsthand the power and breadth that fuzzy decision and expert systems bring to a wide spectrum of unusually difficult problems. I have architected, designed, and programmed three production fuzzy expert systems. These tools have been successfully applied to large, realworld applications in such areas as transportation, managed health care, financial services, insurance risk assessment, database information mining, company stability analysis, multiresource and multiproject management, fraud detection, acquisition suitability studies, new product marketing, and sales analysis. Generally, the final models were less complex, smaller, and easier to build, implement, maintain, and extend than similar systems built using conventional symbolic expert systems. 本資源共5個(gè)可選網(wǎng)絡(luò)硬盤鏈接，7.19 MB。 -------------------------------------------------------------------------------------------------------- https://uploading.com/files/b747 ... 2BFuzzy%2BSystems./ https://rapidshare.com/files/344 ... ning_Fuzzy_Systems. https://www.filefactory.com/file ... Press.p512.1994.rar https://www.easy-share.com/1909173312/The Fuzzy Systems Handbook_A Practitioner's Guide to Building, Using, and Maintaining Fuzzy Systems.Academic Press.p512.1994.rar https://depositfiles.com/files/h1akhbggp -------------------------------------------------------------------------------------------------------- |
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