Machine Learning ‐ Neural Networks, Genetic Algorithms and Fuzzy Systems

Citation

Adeli, H. (1999), "Machine Learning ‐ Neural Networks, Genetic Algorithms and Fuzzy Systems", Kybernetes, Vol. 28 No. 3, pp. 317-318. https://doi.org/10.1108/k.1999.28.3.317.5

This is a book that will be of interest to cyberneticians and systemists. Its contents list which is included below shows a wide range of interconnecting topics that form important studies in any consideration of machine learning. We agree with the authors when they write:

One of the most fascinating and promising developments to emerge in the field of artificial intelligence over the past decade has been machine learning. In this pioneering book, we present a multiparadigm learning approach and demonstrate how the learning performance can be improved substantially through adroit integration of various computing paradigms: neural networks, genetic algorithms, fuzzy sets, and parallel processing. While we concentrate most heavily on neural nets, we describe a number of ingenious new ways of integrating neural network models with other problems solving paradigms to create powerful hybrids with improved learning performance.

The authors claim it is the only book to apply neural nets, genetic algorithms and fuzzy systems to the field of machine learning. It is certainly a unique book and it includes many specific algorithms and also presents many applications image recognition and design.

The contents list is worth perusing: The chapter headings are:

1. 1.

   Introduction.

2. 2.

   Perception Learning with a Hidden Layer.

3. 3.

   An Object‐Oriented Backpropagation Learning Model.

4. 4.

   Concurrent Backpropagation Learning Algorithms.

5. 5.

   An Adaptive Conjugate Gradient Learning Algorithm for Efficient Training of Neural Networks.

6. 6.

   A Concurrent Adaptive Conjugate Gradient Learning Algorithm on MIMD Shared Memory Machines.

7. 7.

   A Concurrent Hybrid Genetic/Neural Nework Learing Algorithm for MIMD Shared Memory Machines.

8. 8.

   A Hybrid Learning Algorithm for Distributed Memory Multicomputers.

9. 9.

   A Fuzzy Neural Nework Learning Model.

The author is a professor at Ohio State University who has published extensively in computer science and engineering. His co‐author, Shih‐Lin Hung is an associate professor at the National Chiao Tung University, Republic of China.