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The Integral WormFolgen

31. Aug 2014•0 gefällt mir•7,782 views

31. Aug 2014•0 gefällt mir•7,782 views

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Technologie

This presentation covers artificial neural networks for artificial intelligence. Topics covered are as follows: artificial neural networks, basic representation, hidden units, exclusive OR problem, backpropagation, advantages of artificial neural networks, properties of artificial neural networks, and disadvantages of artificial neural networks.

The Integral WormFolgen

- 1. Artificial Neural Networks (ANN) • Human information processing takes place through the interaction of many billions of neurons connected to each other, each sending excitatory or inhibitory signals to other neurons (excite in positive/suppress in negative) • Human Brain: Parallel Processing + excites - supresses + - - + + - +
- 2. ANN • The neuron receives signals from other neurons, collects the input signals, and transforms the collected input signal • The single neuron then transmits the transformed signal to other neurons
- 3. ANN • The signals that pass through the junction, known as synapses, are either weakened or strengthened depending upon the strength of the synaptic connection • By modifying synaptic strengths, the human brain is able to store knowledge and thus allow certain inputs to result in specific output or behavior • Translates into a mathematical model • Artificial Neural Networks compare weights – Synopsis is small = - – Synopsis is large = + • ON = + • OFF = - • Neurons are trained – Neurons are on (+) or off (-) • Example: Could be Facial Recognition
- 4. ANN • A basic ANN model consists of – Computational units – Links • A unit emulate the functions of a neuron • Computational units are connected by links with variable weights which represent synapses in the biological model (Human Brain) • Learning Curve: Change synopsis in face recognition – Changes & learns new info
- 5. ANN • The unit receives a weighted sum of all its input via connections and computes its own output value using its own output function • The output value is then propagated to many other units via connection between units
- 6. Basic Representation • Parallel Transfer – Some connections bi-directional, some one-way • Variation of algorithms – 2 levels – Multi-levels • y=f (x1, x2, x3) – where is is a transform function (linear or non-linear)
- 7. Basic Representation Sum: Netj = Sum of Wji Xi Transfer: Yj = F (Netj ) S u m Transfer X1 X2 X3 jth Computational Unit Weights Wj1 Wj2 Wj3 Yj Output Path
- 8. ANN • Computational units in ANN are arranged in layers - input, output, and hidden layers • Units in a hidden layer are called hidden units
- 9. Hidden Units • Hidden unit is a unit which represents neither input nor output variables • It is used to support the required function from input to output
- 13. ANN Learning Algorithm Supervised Learning Unsupervised Learning Binary Input Continued Binary Continued Hopfield Net Perceptron ART I ART II Boltzman- Backpropagation Self-organizing Machine (popular algorithm widely used) Map
- 14. Backpropagation • The algorithm is a learning rule which suggests a way of modifying weights to represent a function from input to output • The network architecture is a feedforward network where computational units are structured in a multi-layered network: an input layer, one or more hidden layer(s), and an output layer
- 15. Backpropagation • The units on a layer have full connections to units on the adjacent layers, but no connection to units on the same layer
- 16. Backpropagation • Calculate the difference (error) between the expected and actual output value • Adjust the weights in order to minimize the error • Minimize the error by performing a gradient decent on the error surface
- 17. Backpropagation • The amount of the weight change for each input pattern in an epoch is proportional to the error • An epoch is completed after the network sees all of the input and output pairs
- 18. Five Input Var. Net Working Capital/Total Assets Retained Earning/Total Assets EBIT/Total Assets Market Value of Common and Preferred Stock/Book Value of Debt Sales/Total Assets Two Output Variables Solvent Firms Bankrupt Firms An ANN model to Predict a Firm’s Bankruptcy
- 19. Advantages of ANN • Parallel Processing • Generalization – a great deal of noise and randomness can be tolerated • Fault tolerance – damage to a few units and weights may not be fatal to the overall network performance
- 20. Properties of ANN • No special recovery mechanism is required for incomplete information • Learning capability
- 21. Disadvantages of ANN • Black box – Difficulty to interpret information on the network • Complicated Algorithms