Inductive Learning Algorithms for Complex System Modeling [Hardcover]

H.R. Madala (Author), Alexey G. Ivakhnenko (Author)

Book Description

ISBN-10: 0849344387 | ISBN-13: 978-0849344381 | Publication Date: January 9, 1994 | Edition: 1

Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and engineering. The book features discussions of algorithm development, structure, and behavior; comprehensive coverage of all types of algorithms useful for this subject; and applications of various modeling activities (e.g., environmental systems, noise immunity, economic systems, clusterization, and neural networks). It presents recent studies on clusterization and recognition problems, and it includes listings of algorithms in FORTRAN that can be run directly on IBM-compatible PCs.

Inductive Learning Algorithms for Complex Systems Modeling will be a valuable reference for graduate students, research workers, and scientists in applied mathematics, statistics, computer science, and systems science disciplines. The book will also benefit engineers and scientists from applied fields such as environmental studies, oceanographic modeling, weather forecasting, air and water pollution studies, economics, hydrology, agriculture, fisheries, and time series evaluations.

Product Details

• Hardcover: 384 pages
• Publisher: CRC-Press; 1 edition (January 9, 1994)
• Language: English
• ISBN-10: 0849344387
• ISBN-13: 978-0849344381
• Product Dimensions: 9 x 6 x 1 inches
• Shipping Weight: 1.4 pounds
• Average Customer Review: 5.0 out of 5 stars  See all reviews (2 customer reviews)
• Amazon Best Sellers Rank: #4,975,305 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

1 of 1 people found the following review helpful:

5.0 out of 5 stars Mahraj who worked with the Inductive Learning Algorithms, October 18, 2000

By A Customer

This review is from: Inductive Learning Algorithms for Complex System Modeling (Hardcover)

The book "Inductive Learning Algorithms for Complex System Modeling" by Hema Madala and Alexei Ivakhnenko decsribes the learning algorithms in the first few chapters. First chapter gives historical perspective of complex nature of the subject. The next few chapters explain the importance of selection criteria and various computing algorithms.

These algorithms are similar to the artificial neural network algorithms which are being used widely in analysis, identification and predicting the fuzzy complex systems; only difference is that they are inductive in nature. They have different strategy in their forecasting approach.

The authors have dealt with the data from some real life complex systems and presented their results. They have given some comparitive results with the functioning of neural networks. Finally the actual source code has been supplied. This is still a valid code and one can convert it to utilize for modern data mining applications which come under the arena of internet CRM applications.

Though this has been first published in 1994, these algorithms are still valid for the modern usage. It would be worthy to consider for publishing a second edition by adding some latest contribution on the topic from the authors.

There is no other book available with such a valuable elaborative information on the topic.

5.0 out of 5 stars Excellent Monograph, May 21, 2004

By A Customer

This review is from: Inductive Learning Algorithms for Complex System Modeling (Hardcover)

It's a collection of fifty years of research in the area of inductive computer-aided algorithms, machine learning and advanced neural networks. The practical examples given in the book are superb. We have used the same concept in our data mining and pattern recognition works. The scripts given in the book with slight modification are very useful in building the networks. I suggest anybody in the specialization of neural networks, machine intelligence and data mining to use this abundent knowledge in their practical works.