X
تبلیغات
الگوریتم ژنتیک

الگوریتم ژنتیک

وبلاگی برای من

Evolutionary Computation in Bioinformatics (The Morgan Kaufmann Series in Artificial Intelligence)

  کتاب زیرو من دارم خواستین بفرستم

http://www.amazon.com/Evolutionary-Computation-Bioinformatics-Artificial-Intelligence/dp/1558607978/ref=sr_1_1?ie=UTF8&s=books&qid=1253560391&sr=1-1-spell

 

 

PART I Introduction to the Concepts of Bioinformatics

and Evolutionary Computation

 

1 An Introduction to Bioinformatics

for Computer Scientists

By David W. Come and Gary B. Fogel

 

 

2 An Introduction to Evolutionary Computation

for Biologists

By Gary B. Fogel and David W. Come

 

 

PART II Sequence and Structure Alignment 39

3 Determining Genome Sequences from Experimental

Data Using Evolutionary Computation

By Jacek Blazewicz and Marta Kasprzak

 

 

4 Protein Structure Alignment Using

Evolutionary Computation

By Joseph D. Szustakowski and Zhipeng Weng

 

 

5 Using Genetic Algorithms for Pairwise and

Multiple Sequence Alignments

By Cidric Notredame

 

 

PART III Protein Folding 113

6 On the Evolutionary Search for Solutions to the

Protein Folding Problem

By Garrison W. Greenwood andJae-Min Shin

 

 

Toward Effective Polypeptide Structure Prediction

with Parallel Fast Messy Genetic Algorithms

By Gary B. Lamont and Laurence D. Merkle

 

 

Application of Evolutionary Computation to

Protein Folding with Specialized Operators

By Steffen Schulze-Kremer

 

 

PART IV Machine Learning and Inference 193

9 Identification of Coding Regions in DNA Sequences

Using Evolved Neural Networks

By Gary B. Fogel, Kumar CheUapilla, and David B. Fogel

 

Clustering Microarray Data with

Evolutionary Algorithms

By Emanuel Falkenauer and Arnaud Marchand

 

Evolutionary Computation and Fractal

Visualization of Sequence Data

By Dan Ashlock and Jim Golden

 

 

Identifying Metabolic Pathways and Gene Regulation

Networks with Evolutionary Algorithms

By Junji Kitagawa and Hitoshi Iba

 

Evolutionary Computational Support for the

Characterization of Biological Systems

By Bogdan Filipi{ andJanez Strancar

 

 

PART V Feature Selection 295

14 Discoveryo f Genetic and Environmental Interactions

in Disease Data Using Evolutionary Computation

By Laetitia Jourdan, Clarisse Dhaenens-Flipo, and E1-Ghazali Talbi

 

Feature Selection Methods Based on Genetic

 

Algorithms for in Silico Drug Design

By Mark J. Embrechts, Muhsin Ozdemir, Larry Lockwood, Curt Breneman,

Kristin Bennett, Dirk Devogelaere, and Marcel Rijckaert

 

Interpreting Analytical Spectra with

Evolutionary Computation

 

ByJemJ. Rowland

 

 

 

 

+ نوشته شده در  دوشنبه سی ام شهریور 1388ساعت 23:58  توسط محسن سیدکاظمی  | 

Artificial Intelligence with Uncertainty

   این کتاب  من دارم خواستین بفرستم 

Contents

Chapter 1

The 50-Year History of Artificial Intelligence ....................................1

1.1 Departure from the Dartmouth Symposium....................................................1

1.1.1 Communication between Different Disciplines...................................1

1.1.2 Development and Growth ....................................................................3

1.2 Expected Goals as Time Goes On...................................................................4

1.2.1 Turing Test ...........................................................................................4

1.2.2 Machine Theorem Proof ......................................................................5

1.2.3 Rivalry between Kasparov and Deep Blue .........................................5

1.2.4 Thinking Machine ................................................................................6

1.2.5 Artificial Life........................................................................................7

1.3 AI Achievements in 50 Years...........................................................................8

1.3.1 Pattern Recognition..............................................................................8

1.3.2 Knowledge Engineering.....................................................................10

1.3.3 Robotics..............................................................................................11

1.4 Major Development of AI in the Information Age .......................................12

1.4.1 Impacts of AI Technology on the Whole Society .............................12

1.4.2 From the World Wide Web to the Intelligent Grid ...........................13

1.4.3 From Data to Knowledge ..................................................................14

1.5 The Cross Trend between AI, Brain Science and Cognitive Science...........15

1.5.1 The Influence of Brain Science on AI...............................................15

1.5.2 The Influence of Cognitive Science on AI........................................17

1.5.3 Coming Breakthroughs Caused by Interdisciplines ..........................18

References................................................................................................................18

Chapter 2

Methodologies of AI ..........................................................................21

2.1 Symbolism Methodology...............................................................................21

2.1.1 Birth and Development of Symbolism .............................................21

2.1.2 Predicate Calculus and Resolution Principle ....................................24

2.1.3 Logic Programming Language ..........................................................26

2.1.4 Expert System ....................................................................................28

2.2 Connectionism Methodology.........................................................................30

2.2.1 Birth and Development of Connectionism ........................................30

2.2.2 Strategy and Technical Characteristics of Connectionism................30

2.2.3 Hopfield Neural Network Model.......................................................33

2.2.4 Back-Propagation Neural Network Model ........................................34

2.3 Behaviorism Methodology.............................................................................35

2.3.1 Birth and Development of Behaviorism............................................35

2.3.2 Robot Control.....................................................................................36

2.3.3 Intelligent Control ..............................................................................37

2.4 Reflection on Methodologies .........................................................................38

References................................................................................................................39

Chapter 3

On Uncertainties of Knowledge ........................................................43

3.1 On Randomness .............................................................................................43

3.1.1 The Objectivity of Randomness ....................................................... 43

3.1.2 The Beauty of Randomness...............................................................46

3.2 On Fuzziness ..................................................................................................47

3.2.1 The Objectivity of Fuzziness .............................................................48

3.2.2 The Beauty of Fuzziness ...................................................................49

3.3 Uncertainties in Natural Languages ..............................................................51

3.3.1 Languages as the Carrier of Human Knowledge ..............................51

3.3.2 Uncertainties in Languages................................................................52

3.4 Uncertainties in Commonsense Knowledge..................................................54

3.4.1 Common Understanding about Common Sense ...............................54

3.4.2 Relativity of Commonsense Knowledge ...........................................55

3.5 Other Uncertainties of Knowledge ................................................................57

3.5.1 Incompleteness of Knowledge...........................................................57

3.5.2 Incoordination of Knowledge ............................................................58

3.5.3 Impermanence of Knowledge............................................................58

References................................................................................................................60

Chapter 4

Mathematical Foundation of AI with Uncertainty ............................61

4.1 Probability Theory .........................................................................................61

4.1.1 Bayes’ Theorem .................................................................................62

4.1.1.1 Relationship and Logical Operation

of Random Event................................................................62

4.1.1.2 Axiomization Definition of Probability .............................63

4.1.1.3 Conditional Probability and Bayes’ Theorem....................64

4.1.2 Probability Distribution Function ......................................................65

4.1.3 Normal Distribution ...........................................................................67

4.1.3.1 The Definition and Properties

of Normal Distribution .......................................................67

4.1.3.2 Multidimensional Normal Distribution ..............................69

4.1.4 Laws of Large Numbers and Central Limit Theorem.......................70

4.1.4.1 Laws of Large Numbers.....................................................70

4.1.4.2 Central Limit Theorem .......................................................71

4.1.5 Power Law Distribution .....................................................................73

4.1.6 Entropy ...............................................................................................74

4.2 Fuzzy Set Theory ...........................................................................................76

4.2.1 Membership Degree and Membership Function ...............................76

4.2.2 Decomposition Theorem and Expanded Principle............................78

4.2.3 Fuzzy Relation ...................................................................................79

4.2.4 Possibility Measure ............................................................................81

4.3 Rough Set Theory ..........................................................................................81

4.3.1 Imprecise Category and Rough Set ...................................................82

4.3.2 Characteristics of Rough Sets............................................................84

4.3.3 Rough Relations.................................................................................86

4.4 Chaos and Fractal...........................................................................................89

4.4.1 Basic Characteristics of Chaos ..........................................................90

4.4.2 Strange Attractors of Chaos...............................................................92

4.4.3 Geometric Characteristics of Chaos and Fractal...............................93

4.5 Kernel Functions and Principal Curves.........................................................94

4.5.1 Kernel Functions ................................................................................94

4.5.2 Support Vector Machine.....................................................................97

4.5.3 Principal Curves ...............................................................................100

References..............................................................................................................104

Chapter 5

Qualitative and Quantitative Transform

Model — Cloud Model ...................................................................107

5.1 Perspectives on the Study of AI with Uncertainty......................................107

5.1.1 Multiple Perspectives on the Study

of Human Intelligence .....................................................................107

5.1.2 The Importance of Concepts in Natural Languages .......................110

5.1.3 The Relationship between Randomness and Fuzziness

in a Concept .....................................................................................110

5.2 Representing Concepts Using Cloud Models..............................................112

5.2.1 Cloud and Cloud Drop.....................................................................112

5.2.2 Numerical Characteristics of Cloud ................................................113

5.2.3 Types of Cloud Model .....................................................................115

5.3 Normal Cloud Generator .............................................................................118

5.3.1 Forward Cloud Generator ................................................................118

5.3.2 Contributions of Cloud Drops to a Concept ...................................123

5.3.3 Understanding the Lunar Calendar’s Solar Terms

through Cloud Models .....................................................................124

5.3.4 Backward Cloud Generator .............................................................125

5.3.5 Precision Analysis of Backward Cloud Generator..........................132

5.3.6 More on Understanding Normal Cloud Model ...............................133

5.4 Mathematical Properties of Normal Cloud .................................................138

5.4.1 Statistical Analysis of the Cloud Drops’ Distribution.....................138

5.4.2 Statistical Analysis of the Cloud Drops’

Certainty Degree ..............................................................................140

5.4.3 Expectation Curves of Normal Cloud .............................................142

5.5 On the Pervasiveness of the Normal Cloud Model.....................................144

5.5.1 Pervasiveness of Normal Distribution .............................................144

5.5.2 Pervasiveness of Bell Membership Function ..................................145

5.5.3 Significance of Normal Cloud .........................................................148

References..............................................................................................................150

Chapter 6

Discovering Knowledge with Uncertainty

through Methodologies in Physics ..................................................153

6.1 From Perception of Physical World to Perception of Human Self ............153

6.1.1 Expressing Concepts by Using Atom Models.................................154

6.1.2 Describing Interaction between Objects by Using Field ................155

6.1.3 Describing Hierarchical Structure of Knowledge

by Using Granularity .......................................................................156

6.2 Data Field.....................................................................................................158

6.2.1 From Physical Field to Data Field ..................................................158

6.2.2 Potential Field and Force Field of Data ..........................................160

6.2.3 Influence Coefficient Optimization of Field Function ....................172

6.2.4 Data Field and Visual Thinking Simulation ....................................178

6.3 Uncertainty in Concept Hierarchy...............................................................182

6.3.1 Discretization of Continuous Data ..................................................183

6.3.2 Virtual Pan Concept Tree.................................................................186

6.3.3 Climbing-Up Strategy and Algorithms............................................188

6.4 Knowledge Discovery State Space ..............................................................196

6.4.1 Three Kinds of State Spaces............................................................196

6.4.2 State Space Transformation .............................................................197

6.4.3 Major Operations in State Space Transformation ...........................199

References..............................................................................................................200

Chapter 7

Data Mining for Discovering Knowledge with Uncertainty...........201

7.1 Uncertainty in Data Mining.........................................................................201

7.1.1 Data Mining and Knowledge Discovery .........................................201

7.1.2 Uncertainty in Data Mining Process ...............................................202

7.1.3 Uncertainty in Discovered Knowledge............................................204

7.2 Classification and Clustering with Uncertainty...........................................205

7.2.1 Cloud Classification .........................................................................206

7.2.2 Clustering Based on Data Field.......................................................213

7.2.3 Outlier Detection and Discovery Based on Data Field...................238

7.3 Discovery of Association Rules with Uncertainty ......................................244

7.3.1 Reconsideration of the Traditional Association Rules ....................244

7.3.2 Association Rule Mining and Forecasting ......................................247

7.4 Time Series Data Mining and Forecasting..................................................253

7.4.1 Time Series Data Mining Based on Cloud Models ........................255

7.4.2 Stock Data Forecasting ....................................................................256

References..............................................................................................................269

Chapter 8

Reasoning and Control of Qualitative Knowledge .........................273

8.1 Qualitative Rule Construction by Cloud .....................................................273

8.1.1 Precondition Cloud Generator and Postcondition

Cloud Generator...............................................................................273

8.1.2 Rule Generator .................................................................................276

8.1.3 From Cases to Rule Generation ......................................................279

8.2 Qualitative Control Mechanism...................................................................280

8.2.1 Fuzzy, Probability, and Cloud Control Methods.............................280

8.2.2 Theoretic Explanation of Mamdani Fuzzy Control Method...........289

8.3 Inverted Pendulum — an Example of Intelligent Control

with Uncertainty...........................................................................................291

8.3.1 Inverted Pendulum System and Its Control.....................................291

8.3.2 Inverted Pendulum Qualitative Control Mechanism .......................292

8.3.3 Cloud Control Policy of Triple-Link Inverted Pendulum ...............294

8.3.4 Balancing Patterns of an Inverted Pendulum ..................................302

References..............................................................................................................312

Chapter 9

A New Direction for AI with Uncertainty ......................................315

9.1 Computing with Words ................................................................................316

9.2 Study of Cognitive Physics..........................................................................320

9.2.1 Extension of Cloud Model...............................................................320

9.2.2 Dynamic Data Field .........................................................................323

9.3 Complex Networks with Small World and Scale-Free Models ..................328

9.3.1 Regularity of Uncertainty in Complex Networks ...........................329

9.3.2 Scale-Free Networks Generation .....................................................332

9.3.3 Applications of Data Field Theory to Networked Intelligence ......339

9.4 Long Way to Go for AI with Uncertainty ...................................................340

9.4.1 Limitations of Cognitive Physics Methodology..............................340

9.4.2 Divergences from Daniel, the Nobel Economics Prize Winner......340

References..............................................................................................................342

Research Foundation Support

............................................................................345

Index

 

+ نوشته شده در  دوشنبه سی ام شهریور 1388ساعت 23:37  توسط محسن سیدکاظمی  | 

Information Modelling and Knowledge Bases XIX: Volume 166 Frontiers in Artificial Intelligence and A

 

   این کتاب  من دارم خواستین بفرستم

 

 

 

+ نوشته شده در  دوشنبه سی ام شهریور 1388ساعت 23:12  توسط محسن سیدکاظمی  | 

Mathematical-Methods-for-Engineers

به نام خدا

 

سلام

فیلم Mathematical-Methods-for-Engineers از دانشگاه ام ای تی رو دارم اگر دوستان خواستن می تونم براشون بفرستم

Lecture #1: Positive Definite Matrices K = A'CA 
Lecture #2: One-dimensional Applications: A = difference matrix
Lecture #3: Network Applications: A = incidence matrix
Lecture #4: Applications to Linear Estimation: least squares 
Lecture #5: Applications to Dynamics: eigenvalues of K, solution of Mu'' + Ku = f
Lecture #6: Underlying Theory: applied linear algebra 
Lecture #7: Discrete vs Continuous: differences and derivatives 
Lecture #8: Applications to Boundary Value Problems: Laplace equation 
Lecture #9:  Solutions of Laplace Equation: complex variables 
Lecture #10: Delta Function and Green's Function
Lecture #11: Initial Value Problems: wave equation and heat equation
Lecture #12: Solutions of Initial Value Problems: eigenfunctions 
Lecture #13: Numerical Linear Algebra: Orthogonalization and A = QR 
Lecture #14: Numerical Linear Algebra: SVD and applications 
Lecture #15: Numerical Methods in Estimation: Recursive least squares and covariance matrix 
Lecture #16: Dynamic Estimation: Kalman filter and square root filter 
Lecture #17: Finite Difference Methods: equilibrium problems
Lecture #18: Finite Difference Methods: stability and convergence
Lecture #19: Optimization and Minimum Principles: Euler equation
Lecture #20: Finite Element Method: equilibrium equations
Lecture #21: Spectral Method: dynamic equations
Lecture #22: Fourier Expansions and Convolution
Lecture #23: Fast Fourier Transform and Circulant Matrices 
Lecture #24: Discrete Filters: lowpass and highpass 
Lecture #25: Filters in the Time and Frequency Domain
Lecture #26: Filter Banks and Perfect Reconstruction
Lecture #27: Multiresolution, Wavelet Transform and Scaling Function
Lecture #28: Splines and Orthogonal Wavelets: Daubechies construction
Lecture #29: Applications in Signal and Image Processing: compression
Lecture #30: Network Flows and Combinatorics: max flow = min cut
Lecture #31: Simplex Method in Linear Programming
Lecture #32: Nonlinear Optimization: algorithms and theory 

 

 

+ نوشته شده در  جمعه سیزدهم شهریور 1388ساعت 12:19  توسط محسن سیدکاظمی  | 

فیلم کلاس درس Principles of Digital Communications I

به نام خدا

 

فیلم کلاس درس Principles of Digital Communications I دانشگاه MIT را دارم اگر دوستان خواستن می تونم براتون بفرستم

1 Introduction: A layered view of digital communication 
2 Discrete source encoding 
3 Memory-less sources, prefix free codes, and entropy 
4 Entropy and asymptotic equipartition property 
5 Markov sources and Lempel-Ziv universal codes 
6 Quantization 
7 High rate quantizers and waveform encoding 
8 Measure, fourier series, and fourier transforms 
9 Discrete-time fourier transforms and sampling theorem Quiz 1 taken 2 days after Ses #9
10 Degrees of freedom, orthonormal expansions, and aliasing 
11 Signal space, projection theorem, and modulation 
12 Nyquist theory, pulse amplitude modulation (PAM), quadrature amplitude modulation (QAM), and frequency translation 
13 Random processes 
14 Jointly Gaussian random vectors and processes and white Gaussian noise (WGN) 
15 Linear functionals and filtering of random processes 
16 Review; introduction to detection Quiz 2 taken 2 days after Ses #16
17 Detection for random vectors and processes 
18 Theorem of irrelevance, M-ary detection, and coding 
19 Baseband detection and complex Gaussian processes 
20 Introduction of wireless communication 
21 Doppler spread, time spread, coherence time, and coherence frequency 
22 Discrete-time baseband models for wireless channels 
23 Detection for flat rayleigh fading and incoherent channels, and rake receivers 
24 Case study — code division multiple access (CDMA) 
25 Review

+ نوشته شده در  جمعه سیزدهم شهریور 1388ساعت 12:12  توسط محسن سیدکاظمی  | 

فیلم کلاس درس معادلات دیفرانسیل دانشگاه ام ای تی MIT

فیلم کلاس درس معادلات دیفرانسیل دانشگاه ام ای اتی رو دارم اگر دوستان خواستن بگن بفرستم

 

 
1 The Geometrical View of y'=f(x,y): Direction Fields, Integral Curves. 
2 Euler's Numerical Method for y'=f(x,y) and its Generalizations. 
3 Solving First-order Linear ODE's; Steady-state and Transient Solutions. 
4 First-order Substitution Methods: Bernouilli and Homogeneous ODE's. 
5 First-order Autonomous ODE's: Qualitative Methods, Applications. 
6 Complex Numbers and Complex Exponentials. 
7 First-order Linear with Constant Coefficients: Behavior of Solutions, Use of Complex Methods. 
8 Continuation; Applications to Temperature, Mixing, RC-circuit, Decay, and Growth Models. 
9 Solving Second-order Linear ODE's with Constant Coefficients: The Three Cases. 
10 Continuation: Complex Characteristic Roots; Undamped and Damped Oscillations. 
11 Theory of General Second-order Linear Homogeneous ODE's: Superposition, Uniqueness, Wronskians. 
12 Continuation: General Theory for Inhomogeneous ODE's. Stability Criteria for the Constant-coefficient ODE's. 
13 Finding Particular Sto Inhomogeneous ODE's: Operator and Solution Formulas Involving Ixponentials. 
14 Interpretation of the Exceptional Case: Resonance. 
15 Introduction to Fourier Series; Basic Formulas for Period 2(pi). 
16 Continuation: More General Periods; Even and Odd Functions; Periodic Extension. 
17 Finding Particular Solutions via Fourier Series; Resonant Terms; Hearing Musical Sounds. 
19 Introduction to the Laplace Transform; Basic Formulas. 
20 Derivative Formulas; Using the Laplace Transform to Solve Linear ODE's. 
21 Convolution Formula: Proof, Connection with Laplace Transform, Application to Physical Problems. 
22 Using Laplace Transform to Solve ODE's with Discontinuous Inputs. 
23 Use with Impulse Inputs; Dirac Delta Function, Weight and Transfer Functions. 
24 Introduction to First-order Systems of ODE's; Solution by Elimination, Geometric Interpretation of a System. 
25 Homogeneous Linear Systems with Constant Coefficients: Solution via Matrix Eigenvalues (Real and Distinct Case). 
26 Continuation: Repeated Real Eigenvalues, Complex Eigenvalues. 
27 Sketching Solutions of 2x2 Homogeneous Linear System with Constant Coefficients. 
28 Matrix Methods for Inhomogeneous Systems: Theory, Fundamental Matrix, Variation of Parameters. 
29 Matrix Exponentials; Application to Solving Systems. 
30 Decoupling Linear Systems with Constant Coefficients. 
31 Non-linear Autonomous Systems: Finding the Critical Points and Sketching Trajectories; the Non-linear Pendulum. 
32 Limit Cycles: Existence and Non-existence Criteria. 
33 Relation Between Non-linear Systems and First-order ODE's; Structural Stability of a System, Borderline Sketching Cases; Illustrations Using Volterra's Equation and Principle. 
 

 

+ نوشته شده در  جمعه سیزدهم شهریور 1388ساعت 12:3  توسط محسن سیدکاظمی  |