Product Details

Synopsis

The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are presently scattered in different fields. There remains a need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. This book attempts to satisfy the above requirement. The algorithms are made clear using MATLAB programs.

Product Identifiers

ISBN-10

1402064098

ISBN-13

9781402064098

Key Details

Author

E. S. Gopi

Number Of Pages

190 pages

Format

EBOOK

Publication Date

2007-09-20

Language

English

Publisher

Springer

Publication Year

2007

Additional Details

Number of Volumes

1 vol.

Copyright Date

2007

Illustrated

Yes

Dimensions

Weight

36.7 Oz

Height

0.2 In.

Width

6.1 In.

Length

9.3 In.

Target Audience

Group

Scholarly & Professional

Classification Method

LC Classification Number

TK1-9971TK5102.9TA16

Table Of Content

Preface. Acknowledgments. Chapter 1 ARTIFICIAL INTELLIGENCE.1 Particle Swarm Algorithm. 1-1 How are the values for the variables ''x'' and ''y'' are updated in every Iteration? 1-2 PSO Algorithm to maximize the function F(X,Y,Z). 1-3 m-Program for PSO Algorithm. 1-4 Program Illustration.2 Genetic Algorithm. 2-1 Roulette Wheel Selection Rule. 2-2 Example. 2-2-1 m-Program for Genetic Algorithm. 2-2-2 Program Illustration. 2-3 Classification of Genetic Operators. 2-3-1 Simple Crossover. 2-3-2 Heuristic Crossover. 2-3-3 Arith crossover.3 Simulated Annealing. 3-1 Simulated Annealing algorithm. 3-2 Example. 3-3 m-program for simulated Annealing.4 Back propagation Neural Network. 4-1 Single Neuron architecture. 4-2 Algorithm. 4-3 Example. 4-4 m-program for training the Artificial Neural Network for the problem proposed in the previous section.5 Fuzzy Logic Systems. 5-1 Union and Intersection of two fuzzy sets. 5-2 Fuzzy logic systems. 5-2-1 Algorithm. 5-3 Why Fuzzy logic systems? 5-4 Example. 5-5 m-program for the realization of fuzzy logic system for the Specifications given in section 5-4.6 Ant Colony Optimization. 6-1 Algorithm. 6-2 Example. 6-3 m-program for finding the optimal order using Ant colony technique for the specifications given in the section 6-2. Chapter 2 PROBABILITY AND RANDOM PROCESS.1 Independent Component Analysis. 1-1 ICA for tow mixed signals. 1-1-1 ICA Algorithm. 1-2 m-program for Independent Component Analysis.2 Gaussian Mixture Model. 2-1 Expectation-Maximization Algorithm. 2-1-1 Expectation stage. 2-1-2 Maximization stage. 2-2 Example. 2-3 m-program for Gaussian Mixture model.3 K-means Algorithm for Pattern recognition. 3-1 K-means Algorithm. 3-2 Example. 3-3 m-program for the k-means Algorithm applied for the example given in section 3-2.4 Fuzzy K-means Algorithm for Pattern recognition. 4-1 Fuzzy k-means Algorithm. 4-2 Example. 4-3 m-program for the Fuzzy k-means algorithm applied for the example given in section 4-2.5 Mean and Variance Normalization. 5-1 Algorithm. 5-2 Example. 5-3 m-program for Mean and Variance Normalization. Chapter 3 NUMERICAL LINEAR ALGEBRA.1 Hotelling Transformation. 1-1 Diagonalization of the matrix ''CM''. 1-2 Example. 1-3 m-program for Hotelling Transformation.2 Eigen Basis. 2-1 Example.3 Singular Value Decomposition. 3-1 Example.4 Projection Matrix. 4-1 Projection of the vector ''a'' on the vector ''b''. 4-2 Projection of the vector on the plane described by the two columns of the matrix ''X''. 4-2-1 Example 1. 4-2-2 Example 2.5 Orthonormal Vectors. 5-1 Gram-Schmidt Orthogonalization procedure. 5-2 Example. 5-3 Need for orthonormal basis. 5-4 m-program for Gram-Schmidt Orthogonalization procedure.6 Computation of the powers of the matrix ''A''.7 Determination of Kth element in the sequence.8 Computation of Exponential of the matrix ''A''.9 Solving Differential equation using Eigen decomposition.10 Computation of Pseudo Inverse of the matrix ''A''.11 Computation of Transformation matrices. 11-1 Transformation matrix for Fourier transformation. 11-2 Transformation matrix for Basis co-efficient transformation. 11-3 Transformation matrix for obtaining co-efficient of Eigen basis. 11-4 Transformation matrix for obtaining co-efficient of Wavelet Basis.12 System stability test using Eigen values.13 Positive definite matrix test for minimal location of the function f(x1, x2, x3, x4...xn)14 Wavelet transformation using matrix method. 14-1 Haar Transformation. 14-1-1 Example. 14-1-2 m-program for Haar forward and inverse transformation. 14-2 Daubechies-4 Transformation. 14-2-1 Example. 14-2-2 m-program for Daubechies-4 forward and inverse transformation. Chapter 4 SELECTED APPLICATIONS.1 Ear Pattern Recognition using Eigen Ears. 1-1 Algorithm. 1-2 m-program for Ear pattern recognition.2 Ear Image data compression using Eigen basis. 2-1 Approach. 2-2 m-program for Ear Image data compression.3 Adaptive Noise filtering using Back propagation Neural Network. 3-1 Approach.

Reviews

From the reviews:The presented book is devoted to the realization of the Digital Signal Processing (DSP) algorithms, using Matlab. â€Š The book is written in such a way that it is suitable for non-mathematical readers and is very much suitable for the beginners who are doing research in Digital Signal Processing. (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1189, 2010)

Synopsis

The Algorithms such as SVD, Eigen decomposition, Gaussian Mixture Model, HMM etc. are presently scattered in different fields. There remains a need to collect all such algorithms for quick reference. Also there is the need to view such algorithms in application point of view. This book attempts to satisfy the above requirement. The algorithms are made clear using MATLAB programs.

Product Identifiers

ISBN-10

1402064098

ISBN-13

9781402064098

Key Details

Author

E. S. Gopi

Number Of Pages

190 pages

Format

EBOOK

Publication Date

2007-09-20

Language

English

Publisher

Springer

Publication Year

2007

Additional Details

Number of Volumes

1 vol.

Copyright Date

2007

Illustrated

Yes

Dimensions

Weight

36.7 Oz

Height

0.2 In.

Width

6.1 In.

Length

9.3 In.

Target Audience

Group

Scholarly & Professional

Classification Method

LC Classification Number

TK1-9971TK5102.9TA16

Table Of Content

Preface. Acknowledgments. Chapter 1 ARTIFICIAL INTELLIGENCE.1 Particle Swarm Algorithm. 1-1 How are the values for the variables ''x'' and ''y'' are updated in every Iteration? 1-2 PSO Algorithm to maximize the function F(X,Y,Z). 1-3 m-Program for PSO Algorithm. 1-4 Program Illustration.2 Genetic Algorithm. 2-1 Roulette Wheel Selection Rule. 2-2 Example. 2-2-1 m-Program for Genetic Algorithm. 2-2-2 Program Illustration. 2-3 Classification of Genetic Operators. 2-3-1 Simple Crossover. 2-3-2 Heuristic Crossover. 2-3-3 Arith crossover.3 Simulated Annealing. 3-1 Simulated Annealing algorithm. 3-2 Example. 3-3 m-program for simulated Annealing.4 Back propagation Neural Network. 4-1 Single Neuron architecture. 4-2 Algorithm. 4-3 Example. 4-4 m-program for training the Artificial Neural Network for the problem proposed in the previous section.5 Fuzzy Logic Systems. 5-1 Union and Intersection of two fuzzy sets. 5-2 Fuzzy logic systems. 5-2-1 Algorithm. 5-3 Why Fuzzy logic systems? 5-4 Example. 5-5 m-program for the realization of fuzzy logic system for the Specifications given in section 5-4.6 Ant Colony Optimization. 6-1 Algorithm. 6-2 Example. 6-3 m-program for finding the optimal order using Ant colony technique for the specifications given in the section 6-2. Chapter 2 PROBABILITY AND RANDOM PROCESS.1 Independent Component Analysis. 1-1 ICA for tow mixed signals. 1-1-1 ICA Algorithm. 1-2 m-program for Independent Component Analysis.2 Gaussian Mixture Model. 2-1 Expectation-Maximization Algorithm. 2-1-1 Expectation stage. 2-1-2 Maximization stage. 2-2 Example. 2-3 m-program for Gaussian Mixture model.3 K-means Algorithm for Pattern recognition. 3-1 K-means Algorithm. 3-2 Example. 3-3 m-program for the k-means Algorithm applied for the example given in section 3-2.4 Fuzzy K-means Algorithm for Pattern recognition. 4-1 Fuzzy k-means Algorithm. 4-2 Example. 4-3 m-program for the Fuzzy k-means algorithm applied for the example given in section 4-2.5 Mean and Variance Normalization. 5-1 Algorithm. 5-2 Example. 5-3 m-program for Mean and Variance Normalization. Chapter 3 NUMERICAL LINEAR ALGEBRA.1 Hotelling Transformation. 1-1 Diagonalization of the matrix ''CM''. 1-2 Example. 1-3 m-program for Hotelling Transformation.2 Eigen Basis. 2-1 Example.3 Singular Value Decomposition. 3-1 Example.4 Projection Matrix. 4-1 Projection of the vector ''a'' on the vector ''b''. 4-2 Projection of the vector on the plane described by the two columns of the matrix ''X''. 4-2-1 Example 1. 4-2-2 Example 2.5 Orthonormal Vectors. 5-1 Gram-Schmidt Orthogonalization procedure. 5-2 Example. 5-3 Need for orthonormal basis. 5-4 m-program for Gram-Schmidt Orthogonalization procedure.6 Computation of the powers of the matrix ''A''.7 Determination of Kth element in the sequence.8 Computation of Exponential of the matrix ''A''.9 Solving Differential equation using Eigen decomposition.10 Computation of Pseudo Inverse of the matrix ''A''.11 Computation of Transformation matrices. 11-1 Transformation matrix for Fourier transformation. 11-2 Transformation matrix for Basis co-efficient transformation. 11-3 Transformation matrix for obtaining co-efficient of Eigen basis. 11-4 Transformation matrix for obtaining co-efficient of Wavelet Basis.12 System stability test using Eigen values.13 Positive definite matrix test for minimal location of the function f(x1, x2, x3, x4...xn)14 Wavelet transformation using matrix method. 14-1 Haar Transformation. 14-1-1 Example. 14-1-2 m-program for Haar forward and inverse transformation. 14-2 Daubechies-4 Transformation. 14-2-1 Example. 14-2-2 m-program for Daubechies-4 forward and inverse transformation. Chapter 4 SELECTED APPLICATIONS.1 Ear Pattern Recognition using Eigen Ears. 1-1 Algorithm. 1-2 m-program for Ear pattern recognition.2 Ear Image data compression using Eigen basis. 2-1 Approach. 2-2 m-program for Ear Image data compression.3 Adaptive Noise filtering using Back propagation Neural Network. 3-1 Approach.

Reviews

From the reviews:The presented book is devoted to the realization of the Digital Signal Processing (DSP) algorithms, using Matlab. â€Š The book is written in such a way that it is suitable for non-mathematical readers and is very much suitable for the beginners who are doing research in Digital Signal Processing. (Tzvetan Semerdjiev, Zentralblatt MATH, Vol. 1189, 2010)