الگوریتم ژنتیک

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

geneticalgorithm — Mon, 21 Sep 2009 20:27:18 GMT

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

Artificial Intelligence with Uncertainty

geneticalgorithm — Mon, 21 Sep 2009 20:06:18 GMT

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

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

geneticalgorithm — Mon, 21 Sep 2009 19:41:18 GMT

این کتاب من دارم خواستین بفرستم

Mathematical-Methods-for-Engineers

geneticalgorithm — Fri, 04 Sep 2009 08:48:18 GMT

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

فیلم کلاس درس Principles of Digital Communications I

geneticalgorithm — Fri, 04 Sep 2009 08:41:18 GMT

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

فیلم کلاس درس معادلات دیفرانسیل دانشگاه ام ای تی MIT

geneticalgorithm — Fri, 04 Sep 2009 08:32:18 GMT

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.

Introduction to Algorithms

geneticalgorithm — Tue, 21 Jul 2009 17:29:18 GMT

به نام خدا

با سلام

فیلم ویدویی کلاس درس الگوریتم دانشگاه ام ای تی رو دارم دوستان اگر خواستن بگن براشون بفرستم

1 Administrivia - Introduction - Analysis of Algorithms, Insertion Sort, Mergesort
2 Asymptotic Notation - Recurrences - Substitution, Master Method
3 Divide-and-Conquer: Strassen, Fibonacci, Polynomial Multiplication
4 Quicksort, Randomized Algorithms
5 Linear-time Sorting: Lower Bounds, Counting Sort, Radix Sort
6 Order Statistics, Median
7 Hashing, Hash Functions
8 Universal Hashing, Perfect Hashing
9 Relation of BSTs to Quicksort - Analysis of Random BST
10 Red-black Trees, Rotations, Insertions, Deletions
11 Augmenting Data Structures, Dynamic Order Statistics, Interval Trees
12 Skip Lists
13 Amortized Algorithms, Table Doubling, Potential Method
14 Competitive Analysis: Self-organizing Lists
15 Dynamic Programming, Longest Common Subsequence
16 Greedy Algorithms, Minimum Spanning Trees
17 Shortest Paths I: Properties, Dijkstra's Algorithm, Breadth-first Search
18 Shortest Paths II: Bellman-Ford, Linear Programming, Difference Constraints
19 Shortest Paths III: All-pairs Shortest Paths, Matrix Multiplication, Floyd-Warshall, Johnson
20 Quiz 2 Review
21 Ethics, Problem Solving (Mandatory Attendance)
22 Advanced Topics
23 Advanced Topics (cont.)
24 Advanced Topics (cont.)
25 Advanced Topics (cont.) - Discussion of Follow-on Classes

کنفرانس انگلیس

geneticalgorithm — Tue, 18 Dec 2007 17:27:18 GMT

به نام الله

سلام

یه مقاله فرستاده بودم به کنفرانس هوش مصنوعی تو کمبریج خوب بد بختانه اکسپ خورده حالا موندم باز چی کار کنم نه می تونم برم نه می تونم حمایت مالی فقط برای ریجستر داشته باشم هیچی ....

راستی قرار پروفسور لطفی علی‌عسکرزاده معروف به زاده (Zadeh) سخنراني كنه

The basic ideas underlying soft computing in its current incarnation have links to many earlier influences, among them Prof. Zadeh’s 1965 paper on fuzzy sets; the 1973 paper on the analysis of complex systems and decision processes; and the 1979 report (1981 paper) on possibility theory and soft data analysis

BISC Program is the world-leading center for basic and applied research in soft computing. The principal constituents of soft computing (SC) are fuzzy logic (FL), neural network theory (NN) and probabilistic reasoning (PR), with the latter subsuming belief networks, evolutionary computing including DNA computing, chaos theory and parts of learning theory. Some of the most striking achievements of BISC Program are: fuzzy reasoning (set and logic), new soft computing algorithms making intelligent, semi-unsupervised use of large quantities of complex data, uncertainty analysis, perception-based decision analysis and decision support systems for risk analysis and management, computing with words, computational theory of perception (CTP), and precisiated natural language (PNL).

استخوان

geneticalgorithm — Wed, 24 Oct 2007 16:09:52 GMT

بسم الله الرحمن الرحیم

سلام

هر روز ممکنه همه ما استخون ببنیم مثلا تو غذا یا حتی بلغ خیابان گربه ها کسیه زباله رو پاره می کنن و کلی اشغال می ریزه بیرون تو کوچه خیابون بحث من زباله نیس بحث من استخون هست و زیبایی ها خلقتش یک مدت رو یک پروژه دارم کار می کنم در این رابطه طبیعت با کمترین مصالح بهترین یا بهتر بگیم بهینه ترین ساختار ها رو پید می اره و این مسئله در استخون خیلی زیبا نمایان شده البته رشته من مکانیک هم نیس فقط دنبال این الگوریتم های بهینه هستم تا بتونم شاید یکسری مسایل کنترلی رو بهتر حل کنم و از طرف دیگه سعی می کنم با انجام یکسری کارای کنترلی در شبیه سازی ساختاری استخوان که در مهندسی مکانیک و عمران می تونه خیلی مورد بهره برداری باشه به اهداف ویژه ای برسم ای کاش یکم وقت بیشتری داشتم ای کاش می تونستم با یک تیم درست حسابی که حداقل یک مهندس مکانیک داشته باشه کار کنم البته دادشم عمران می خونه دیگه داره تموم می کنه ولی فعلا وقت نداره برای ارشد می خونه و هزار ای کاش

شاید بعد تموم شدن پروژه بیشتر بنویسم .

2000 بازدید

geneticalgorithm — Fri, 12 Oct 2007 17:52:59 GMT

بسم الله الرحمن الرحیم

سلام

۲۰۰۰ بازدید هم صورت گرفت می خواستم وبلاگ و حذف کنم ولی گفتم شاید نظر تغییر کنه چند روز صبر کنم تا عاقلانه تر تصمیم بگیریم واقعیتش خیلی وقت و انرژی ازم میگیره دیگه دل دماغشو ندارم .