1 / 14

Textbook s

Introduction to Neural Networks and Fuzzy Logic. Textbook s. Textbook: “ Neural Networks. A Comprehensive Foundation ”, 2nd Edition, Simon Haykin, Prentice Hall , 1999 . “ Fuzzy Systems Theory and Its Application ”, Toshiro Terano et. al., Academic Press , 1992 .

cree
Download Presentation

Textbook s

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Introduction to Neural Networks and Fuzzy Logic Textbooks Textbook: “Neural Networks. A Comprehensive Foundation”, 2nd Edition, Simon Haykin, Prentice Hall, 1999. “Fuzzy Systems Theory and Its Application”, Toshiro Terano et. al., Academic Press, 1992.

  2. Introduction to Neural Networks and Fuzzy Logic Grade Policy • Final Grade = 20% Homework + 20% Quizzes + 30% Midterm Exam + 30% Final Exam + Extra Points • Homeworks will be given in fairly regular basis. The average of homework grades contributes 20% of final grade. • Written homeworks are to be submitted on A4 papers, otherwise they will not be graded. • Homeworks must be submitted on time. If you submit late, < 10 min.  No penalty 10 – 60 min.  –20 points > 60 min.  –40 points • There will be 3 quizzes. Only the best 2 will be counted. The average of quiz grades contributes 20% of final grade. • Midterm and final exam schedule will be announced in time. • Make up of quizzes and exams will be held one week after the schedule of the respective quizzes and exams.

  3. Introduction to Neural Networks and Fuzzy Logic Grade Policy • The score of a make up quiz or exam, upon discretion, can be multiplied by 0.9 (the maximum score for a make up is then 90). • Extra points will be given if you solve a problem in front of the class. You will earn 1, 2, or 3 points. • Lecture slides can be copied during class session. It is also available on internet. Please check the course homepage regularly. http://zitompul.wordpress.com • Heading of Written Homework Papers (Required)

  4. Neural Networks Introduction Introduction to Neural Networks Validation: • Generally, means confirming that a product or service meets the needs of its users. • Testing whether the mathematical model is good enough or not to describe the empirical phenomenon.

  5. Neural Networks Introduction Experimental Modeling • Experimental modelingconsists of three steps: • The choice of model class • The choice of model structures (number of parameters, model order, time delay) • The calculation of the parameters and time delay. • The model may be chosen to be linear, nonlinear, or multi locally-linear. • A-priori (prior, previous) knowledge of the system to be modeled is required in most cases. • Artificial Neural Networks (or simply Neural Networks) offers a general solution for experimental modeling.

  6. Neural Networks Introduction Experimental Modeling Using Neural Networks • A neural network is a massively-parallel distributed processor made up of simple processing unit, which has natural propensity for storing experiential knowledge and making it available for use. • It resembles the brain in two respects: • Knowledge is acquired by the network from its environment through a learning process. • Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge.

  7. Neural Networks Introduction Biological and Artificial Neuron Structure of Biological neuron Activation function Structure of Artificial neuron

  8. Neural Networks Introduction Activation Function • Any continuous (differentiable) function can be used as an activation function in a neural network. • The nonlinear behavior of the neural networks is inherited from the used nonlinear activation functions. Tangent sigmoid function Logarithmic sigmoid function Radial basis function Linear function

  9. Neural Networks Introduction Network Architectures Single layer feedforward network (Single layer perceptron) Input layer Output layer Multilayer feedforward network (Multilayer perceptron) Input layer Hidden layer Output layer

  10. Neural Networks Introduction Network Architectures Diagonal recurrent networks Fully recurrent networks Input layer Hidden layer Output layer Input layer Hidden layer Output layer Delay element in arecurrent network

  11. Neural Networks Introduction Network Architectures Elman’s recurrent networks Jordan’s recurrent networks

  12. Neural Networks Introduction Preparation Assignment • Ensure yourself to install Matlab 7 or newer in your computer, along with Matlab Simulink, Control System Toolbox, and Fuzzy Logic Toolbox. • Quizzes, Midterm Exam, and Final Exam will be computer-based.

  13. Neural Networks Introduction Homework 1A • Make 3 groups. Then, conduct an internet research and prepare a short PowerPoint presentation about the following topics: • The timeline/milestones in neural networks development. • The applications and implementations of neural networks. • The current trends/methods of neural networks research. • Each group will be given 15 minutes time for presentation on Tuesday, 15.01.2012.

More Related