Neural Networks

References

• Neural network design, M. T. Hagan & H. B. Demuth & M. Beale PWS publishing company – 1996.

• Neural network: A comprehensive foundation, Simon Haykin, Prentice Hall – 1999.

• Application of Neural Networks to Adaptive Control of Nonlinear Systems, G. W. Ng, John Wiley & Sons Ltd, - 1997.

Other Refrences

• Intelligent Control Based on Flexible Neural Networks, M. Teshnehlab, K. Watanabe, Kluwer Academic Publication, 1999.

• Neural Network for pattern recognition, Christopher Bishop, Clarendon Press Oxford – 1995.

• Nonlinear system Identification (from classical Approacches to neural network and fuzzy models), Oliver Nelles, Springer – 2001.

• Computational Intelligence, Andries P. Engelbercht, John Wiley & Sons Ltd, - 2002.

Syllabuses

• Introduction: Objectives, History, Applications, Biological inspiration, Math inspiration!

• Neural Network (NN) & Neuron Model: Feed Forward NN, Recurrent NN, Hamming NN, Hopfield NN, Single Layer Perceptron, and Multi Layer Perceptron.

• Learning Algorithms– Supervised Learning: early learning algorithms, first order

gradient methods, second order gradient methods, LRLS and IGLS algorithms.

– Unsupervised Learning: hebbian learning, Boltzman machine, kohonen self- organizing learning.

– Population based optimization: genetic algorithms, ant colony, particle swarm optimization (PSO).

Syllabuses 2

• Applicable NN– Recurrent NN: sub connection NN, Elman NN, Jordan NN,

Elman- Jordan NN, and flexible recurrent NN. – Neuro- fuzzy network: RBF, NRBF, GMDH (adline), ANFIS,

LLM.– Dynamic and memorized NN: dynamic neuron models,

Gamma, CMAC.• Where are these NN used?

– NN control strategies– Identification– Recognition

تمرين ها

7همزمان با معرفي بخشهاي مختلف درس •سري تمرين در طول ترم داده خواهد شد. قسمتي از اين تمرينات به فرم پروژه هاي

( کامپيوتري، قابل انجام با mini projectکوچک ) خواهد بود. هدف آشنايي MATLABنرم افزار

آموزش، ساختارهاي بيشتر با الگوريتمهايمتفاوت شبکه هاي عصبي و کاربرد هاي

گوناگون شبکه هاي معرفي شده، مي باشد.

پروژه نهايي

مهمترين قسمت درس و خروجي نهايي آن پروژه اي تحقيقاتي •در زمينه کاربرد شبکه هاي عصبي است. پروژه به صورت تک نفره ويا حداکثر دو نفره تعريف و انجام مي شود. محدوديتي در انتخاب موضوع پروژه وجود ندارد، به غير از آنکه حتما بايد از روشها و موضوعات مطرح شده در درس استفاده شود و

سعي کنيد از موضوعات جديد و نو استفاده کنيد. تعريف پروژه خود را حتما با من هماهنگ کنيد و آنرا به صورت يک پيشنهاد

-شرح 2-عنوان پروژه 1 در سه قسمت A4پروژه در يک برگ -کارهاي انجام شده در اين 3مختصر و نوآوري مد نظر شما

مرجع اصلي خود را ذکر کنيد(. موضوع پروژه 4زمينه )حداقل e-mailانتخاب نموده و به من آذر30حداکثر تا تاريخ خود را

مي باشد اسفند15حداکثر تا تاريخ بزنيد. مهلت تحويل پروژه و به هيچ عنوان تمديد نخواهد شد.

امتحان ميان ترم و پايان ترم

در اواخر آذر ماه امتحان ميان ترم برگزار •مي شود که بخش عمده آن به مباحث

تئوري و نظري شبکه هاي عصبي مر بوط مي شود و امتحان پايان ترم به صورت

Take Home سري 2 مي باشد و معادل mini project .است

e-mailشيوه ارسال

تکاليف و پروژه هاي خود را به آدرس •network.neural@gmail.com

خود حتما e-mailارسال کنيد و در عنوان شماره پروزه را ذکر کنيد. همچنين تمام

نموده و نام zipفايالها و کد نوشته شده را خود را به آن فايل اختصاص دهيد.

شيوه ارزيابي

40 % تمرينات و امتحان پايان ترم:• 25 % امتحان ميان ترم:•

40 % پروژه نهايي:•

Introduction

Historical Sketch

• Pre-1940: von Hemholtz, Mach, Pavlov, etc.– General theories of learning, vision, conditioning– No specific mathematical models of neuron operation

• 1940s: Hebb, McCulloch and Pitts– Mechanism for learning in biological neurons– Neural-like networks can compute any arithmetic function

• 1950s: Rosenblatt, Widrow and Hoff– First practical networks and learning rules

• 1960s: Minsky and Papert– Demonstrated limitations of existing neural networks, new learning algorithms are

not forthcoming, some research suspended• 1970s: Amari, Anderson, Fukushima, Grossberg, Kohonen

– Progress continues, although at a slower pace• 1980s: Grossberg, Hopfield, Kohonen, Rumelhart, etc.

– Important new developments cause a resurgence in the field

Applications• Aerospace

– High performance aircraft autopilots, flight path simulations, aircraft control systems, autopilot enhancements, aircraft component simulations, aircraft component fault detectors

• Automotive– Automobile automatic guidance systems,

warranty activity analyzers

• Banking– Check and other document readers, credit

application evaluators

• Defense– Weapon steering, target tracking, object

discrimination, facial recognition, new kinds of sensors, sonar, radar and image signal processing including data compression, feature extraction and noise suppression, signal/image identification

• Electronics– Code sequence prediction, integrated circuit

chip layout, process control, chip failure analysis, machine vision, voice synthesis, nonlinear modeling

Applications

• Financial– Real estate appraisal, loan advisor, mortgage screening, corporate bond

rating, credit line use analysis, portfolio trading program, corporate financial analysis, currency price prediction

• Manufacturing– Manufacturing process control, product design and analysis, process and

machine diagnosis, real-time particle identification, visual quality inspection systems, beer testing, welding quality analysis, paper quality prediction, computer chip quality analysis, analysis of grinding operations, chemical product design analysis, machine maintenance analysis, project bidding, planning and management, dynamic modeling of chemical process systems

• Medical– Breast cancer cell analysis, EEG and ECG analysis, prosthesis design,

optimization of transplant times, hospital expense reduction, hospital quality improvement, emergency room test advisement

Applications

• Robotics– Trajectory control, forklift robot, manipulator controllers, vision systems

• Speech– Speech recognition, speech compression, vowel classification, text to

speech synthesis• Securities

– Market analysis, automatic bond rating, stock trading advisory systems• Telecommunications

– Image and data compression, automated information services, real-time translation of spoken language, customer payment processing systems

• Transportation– Truck brake diagnosis systems, vehicle scheduling, routing systems

Do a work without thinking!!!!!

Biology Inspiration

Cell body

Axon

Synapse

Dendrites

• Neurons respond slowly – 10-3 s compared to 10-9 s for

electrical circuits

• The brain uses massively parallel computation– 1011 neurons in the brain– 104 connections per neuron

• Human nervous system is built of cells call neuron Each neuron can receive, process and transmit electrochemical signals Dendrites extend from the cell body to other neurons, and the connection point is called synapse Signals received among dendrites are transmitted to and summed in the cell body If the cumulative excitation exceed a threshold, the cell fires, which sends a signal down the axon to other neurons

Math Inspiration

• Radial basis model introduce as a general function approximator:

• If desired, the biases wko can be absorbed into the summation by including an extra basis function do whose activation is set to 1. For the case of Gaussian basis functions we have

• Hartman et al. (1990) give a formal proof of this property for networks with Gaussian basis functions in which the widths of the Gaussians are treated as adjustable parameters. A more general result was obtained by Park and Sandberg (1991) who show that, with only mild restrictions on the form of the kernel functions, the universal approximation property still holds. Further generalizations of this results are given in (Park and Sandberg, 1993).

Transfer Functions

Transfer Functions

Transfer Functions

Multiple-Input Neuron

Layer of Neurons

Abbreviated Notation