Download Fuzzy neural networks for real time control applications : by Erdal Kayacan, Mojtaba Ahmadieh Khanesar PDF

By Erdal Kayacan, Mojtaba Ahmadieh Khanesar

AN essential source FOR ALL those that layout AND enforce TYPE-1 AND TYPE-2 FUZZY NEURAL NETWORKS IN genuine TIME SYSTEMS

Delve into the type-2 fuzzy common sense platforms and develop into engrossed within the parameter replace algorithms for type-1 and type-2 fuzzy neural networks and their balance research with this book!

Not simply does this e-book stand except others in its concentration but additionally in its application-based presentation type. ready in a fashion that may be simply understood by way of those people who are skilled and green during this box. Readers can enjoy the laptop resource codes for either identity and regulate reasons that are given on the finish of the book.

A transparent and an in-depth exam has been made from the entire invaluable mathematical foundations, type-1 and type-2 fuzzy neural community constructions and their studying algorithms in addition to their balance research.

You will locate that every bankruptcy is dedicated to another studying set of rules for the tuning of type-1 and type-2 fuzzy neural networks; a few of which are:

• Gradient descent

• Levenberg-Marquardt

• prolonged Kalman filter

In addition to the aforementioned traditional studying equipment above, variety of novel sliding mode keep an eye on theory-based studying algorithms, that are easier and feature closed types, and their balance research were proposed. additionally, hybrid tools along with particle swarm optimization and sliding mode regulate theory-based algorithms have additionally been introduced.

The strength readers of this publication are anticipated to be the undergraduate and graduate scholars, engineers, mathematicians and desktop scientists. not just can this booklet be used as a reference resource for a scientist who's drawn to fuzzy neural networks and their real-time implementations but additionally as a direction ebook of fuzzy neural networks or man made intelligence in grasp or doctorate collage reviews. we are hoping that this publication will serve its major goal successfully.

  • Parameter replace algorithms for type-1 and type-2 fuzzy neural networks and their balance analysis
  • Contains algorithms which are appropriate to genuine time systems
  • Introduces quick and straightforward variation principles for type-1 and type-2 fuzzy neural networks
  • Number of case experiences either in identity and control
  • Provides MATLAB® codes for a few algorithms within the book

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00004-9 Copyright © 2016 Elsevier Inc. All rights reserved. 37 38 Fuzzy Neural Networks for Real Time Control Applications where x1 , x2 , . . ,xn are the input variables, uj ’s are the output variables, and Aji ’s are type-1 fuzzy sets for the jth rule and the ith input. The parameters in the consequent part of the rules are wij and bj (i = 1, . . , n, j = 1, . . , M). 3) in which ∗ represents the t-norm, which is the prod operator in this book. 1) can be classified into three groups [1]: 1.

7 (1999) 643-658. M. Mendel, R. John, F. Liu, Interval Type-2 fuzzy logic systems made simple, IEEE Trans. Fuzzy Syst. 14 (2006) 808-821. -F. -H. Hsu, Reinforcement interval Type-2 fuzzy controller design by online rule generation and Q-value-aided ant colony optimization, IEEE Trans. Syst. Man Cybernet. B Cybernet. 39 (6) (2009) 1528-1542. [4] C. Juang, Y. Tsao, A self-evolving interval Type-2 fuzzy neural network with online structure and parameter learning, IEEE Trans. Fuzzy Syst. 16 (2008) 1411-1424.

B Cybernet. 38 (6) (2008) 1537-1548. [15] Q. Mendel, Interval type-2 fuzzy logic systems: theory and design, IEEE Trans. Fuzzy Syst. 8 (2000) 535-550. N. M. Mendel, Operations on type-2 fuzzy sets, Fuzzy Sets Syst. 122 (2) (2001) 327-348. ” The advantages of ANNs such as learning capability from input-output data, generalization capability, and robustness and the advantages of fuzzy logic theory such as using expert knowledge are harmonized in FNNs. In this chapter, type-1 and type-2 TSK fuzzy logic models are introduced.

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