Neural fuzzy control systems with structure and parameter learning (Record no. 72431)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 02549nmm a2200373Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | 00002225 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220711214101.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 181207s1994 si a ob 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789814354240 |
| -- | (ebook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | (hbk.) |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 629.8 |
| 100 1# - AUTHOR NAME | |
| Author | Lin, C. T. |
| 245 10 - TITLE STATEMENT | |
| Title | Neural fuzzy control systems with structure and parameter learning |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | Singapore : |
| Publisher | World Scientific Publishing Co. Pte Ltd., |
| Year of publication | ©1994. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource (144 p.) : |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "A general neural-network-based connectionist model, called Fuzzy Neural Network (FNN), is proposed in this book for the realization of a fuzzy logic control and decision system. The FNN is a feedforward multi-layered network which integrates the basic elements and functions of a traditional fuzzy logic controller into a connectionist structure which has distributed learning abilities. In order to set up this proposed FNN, the author recommends two complementary structure/parameter learning algorithms: a two-phase hybrid learning algorithm and an on-line supervised structure/parameter learning algorithm. Both of these learning algorithms require exact supervised training data for learning. In some real-time applications, exact training data may be expensive or even impossible to get. To solve this reinforcement learning problem for real-world applications, a Reinforcement Fuzzy Neural Network (RFNN) is further proposed. Computer simulation examples are presented to illustrate the performance and applicability of the proposed FNN, RFNN and their associated learning algorithms for various applications."-- |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://www.worldscientific.com/worldscibooks/10.1142/2225#t=toc |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 588 ## - | |
| -- | Title from web page (viewed December 7, 2018). |
| 520 ## - SUMMARY, ETC. | |
| -- | Publisher's website. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Intelligent control systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Fuzzy systems. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Neural networks (Computer science) |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electronic books. |
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