1. neuro-fuzzy and soft computing, a computational approach to learning and machine intelligence
Comment: This book is jyh-shing, a good student at Lofti A. Zadeh (Foundation of Fuzzy Set ).
Written by Roger Jang (JI zhixing), and zadel personally introduced the preface ~~~ Although the publishing year-1997 was earlier, it was also 32 years after the advent of Fuzzy Set-1965. The most popular one is the author's self-developed adaptive neuro fuzzy Interface System (anfs ), it pioneered neuro-fuzzy (neural-fuzzy). Even Wikipedia directly said it was its founder ~~~
Content: artificial neural networks, fuzzy inference system, how to combine and create 1 + 1> 2, and instant heuristic search algorithms (genetic algorithm, Ga, etc ), in this way, we will see 1 + 1 + 1> 3.
Clustering )~~~
On the homepage of a book, download e-books and their materials, and attach the most successful field of Fuzzy Logic Theory-a list of the kaishan of Fuzzy Logic Control (FLC). Let's see who is the originator ~~~ PS: I reviewed fuzzy and only read chapter 4 of Part I ~~~ Continue to work toward neuro-fuzzy ~~~ Then, neuro-fuzzy-PSO ~~~ Protocol 2. Differential Evolution: a practical approach to global optimization
Comments: Differential Evolution (de) algorithms play a leading role in Evolutionary Algorithms (EAS-evolutionary algorithms). I personally think that they are second only to GA (and there may be PSO ). The authors of this book, Jian Ming v. Price Rainer M. Storn Jouni A. lampinen, price and Storn, are the creators of the De algorithm, and you will know the quality of the book. Content: there are more than 500 pages in all the chapters of this book, but the content of the first three chapters is the main foundation. The following applications can be used to give a brief introduction as needed: chapter 1. the motivation for differential evolution -- mainly reviews some traditional optimization algorithms and modern algorithms, introduces the general development history, and finally introduces the most basic version of de
Chapter 2. The differential evolution algorithm -- this chapter is the topic of the De algorithm. It basically analyzes the influence of each parameter on the De effect. I personally think a keyword is "vecor-based ".
Chapter 3. Benchmarking differential evolution-compared with other modern optimization algorithms, it is worth noting that many benchmark functions are provided. The author seems to be a member of the iceo committee.
Ebook download on the book homepage PS: de algorithm is really simple and efficient ~~~ Bytes --------------------------------------------------------------------------------------------------------------------------
3. metaheuristics-from design to implementation
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4. Artificial Intelligence: A Guide to intelligent systems (second edition)
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5. Computational Intelligence concepts to implementations
To be continued ~~~
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