<p>This book presents new concepts and implementations of Computational Intelligence (CI) systems (based on neuro-fuzzy and fuzzy neural synergisms) and a broad comparative analysis with the best-known existing neuro-fuzzy systems as well as with systems representing other knowledge-discovery techni
Fuzzy and Neuro-Fuzzy Intelligent Systems
β Scribed by Professor Ernest CzogaΕa Ph.D., D.Sc., Professor Jacek ΕΔski Ph.D., D.Sc. (auth.)
- Publisher
- Physica-Verlag Heidelberg
- Year
- 2000
- Tongue
- English
- Leaves
- 206
- Series
- Studies in Fuzziness and Soft Computing 47
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
β¦ Synopsis
Intelligence systems. We perfonn routine tasks on a daily basis, as for example: β’ recognition of faces of persons (also faces not seen for many years), β’ identification of dangerous situations during car driving, β’ deciding to buy or sell stock, β’ reading hand-written symbols, β’ discriminating between vines made from Sauvignon Blanc, Syrah or Merlot grapes, and others. Human experts carry out the following: β’ diagnosing diseases, β’ localizing faults in electronic circuits, β’ optimal moves in chess games. It is possible to design artificial systems to replace or "duplicate" the human expert. There are many possible definitions of intelligence systems. One of them is that: an intelligence system is a system able to make decisions that would be regarded as intelligent ifthey were observed in humans. Intelligence systems adapt themselves using some example situations (inputs of a system) and their correct decisions (system's output). The system after this learning phase can make decisions automatically for future situations. This system can also perfonn tasks difficult or impossible to do for humans, as for example: compression of signals and digital channel equalization.
β¦ Table of Contents
Front Matter....Pages i-xvi
Classical sets and fuzzy sets Basic definitions and terminology....Pages 1-26
Approximate reasoning....Pages 27-64
Artificial neural networks....Pages 65-92
Unsupervised learning Clustering methods....Pages 93-127
Fuzzy systems....Pages 129-139
Neuro-fuzzy systems....Pages 141-162
Applications of artificial neural network based fuzzy inference system....Pages 163-180
Back Matter....Pages 181-196
β¦ Subjects
Artificial Intelligence (incl. Robotics); Business Information Systems
π SIMILAR VOLUMES
Springer, 2002. β 367.<div class="bb-sep"></div>Traditional Artificial Intelligence (AI) systems adopted symbolic processing as their main paradigm. Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation. Unfortunately, these syste
Neural Fuzzy Systems provides a comprehensive, up-to-date introduction to the basic theories of fuzzy systems and neural networks, as well as an exploration of how these two fields can be integrated to create Neural-Fuzzy Systems. It includes Matlab software, with a Neural Network Toolkit, and a Fuz
Fuzzy and Neuro-Fuzzy Systems in Medicineprovides a thorough review of state-of-the-art techniques and practices, defines and explains relevant problems, as well as provides solutions to these problems.After an introduction, the book progresses from one topic to another - with a linear development f