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KULIAH II JST: BASIC CONCEPTS

KULIAH II JST: BASIC CONCEPTS. Amer Sharif, S.Si, M.Kom. INTRODUCTION REVIEW. Neural Network definition: A massively parallel distributed processor of simple processing units (neuron) Store experiential knowledge and make it available for use

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KULIAH II JST: BASIC CONCEPTS

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  1. KULIAH II JST:BASIC CONCEPTS Amer Sharif, S.Si, M.Kom

  2. INTRODUCTION REVIEW • Neural Network definition: • A massively parallel distributed processor of simple processing units (neuron) • Store experiential knowledge and make it available for use • Knowledge is acquired from the environment through learning process • Knowledge is stored as internerneuron connection strengths (synaptic weights)

  3. INTRODUCTION REVIEW • Benefits: • Nonlinearity • Input Output Mapping • Adaptivity • Evidential Response • Contextual Information • Fault Tolerance/Graceful Degrading • VLSI Implementability • Uniform Analysis & Design

  4. NEURON MODELLING Basic elements of neuron: • A set of synapses or connecting links • Each synapse is characterized by its weight • Signal xjat synapse j connected to neuron kis multiplied by synaptic weight wkj • Biasis bk • An adder for summing the input signals • An activation function for limiting the output amplitude of the neuron

  5. NEURON MODELLING • Block diagram of a nonlinier neuron

  6. NEURON MODELLING Note • x1, x2,…, xmare input signals • wk1, wk2,…, wkmare synaptic weights of neuron k • uk is the linier combiner output • bk is bias • is the activation function • yk is the output signal of the neuron

  7. NEURON MODELLING • If and bias is substituted for a synapse where x0= + 1 with weight wk0 = bk then and

  8. NEURON MODELLING • Modified block diagram of a nonlinier neuron

  9. 1.2 1 0.8 0.6 0.4 0.2 0 -2 -1 0 1 2 v ACTIVATION FUNCTIONS Activation Function types: • Threshold Function and • also known as the McCulloch-Pitts model

  10. ACTIVATION FUNCTIONS • Piecewise-Linear Function

  11. ACTIVATION FUNCTIONS • Sigmoid Function • S-shaped • Sample logistic function: • ais the slope parameter: the larger athe steeper the function • Differentiable everywhere increasing a

  12. wkj yk= wkj xj xj xj yi yk=yi + yj yj xj xj xj NEURAL NETWORKS AS DIRECTED GRAPHS • Neural networks maybe represented as directed graphs: • Synaptic links (linier I/O) • Activation links (nonlinier I/O) • Synaptic convergence • Synaptic divergence

  13. x0 =+1 x1 Output yk x2 . . . xm NEURAL NETWORKS AS DIRECTED GRAPHS • Architectural graph: partially complete directed graph

  14. FEEDBACK • Output of a system influences some of the input applied to the system • One or more closed paths of signal transmission around the system • Feedback plays an important role in recurrent networks

  15. w x’j (n) yk(n) xj(n) z-1 FEEDBACK • Sample single-loop feedback system • Output signal yk(n) is an infinite weighted summation of present and past samples of input signal xj(n) wis fixed weight z-1is unit-delay operator is sample of input signal delayed by l time units

  16. yk(n) wxj(0) 0 1 2 3 4 n FEEDBACK • Dynamic system behavior is determined by weight w w < 1 • w < 1 • System isexponentially convergent/stable • System posses infinite memory: Output depends on input samples extending into the infinite past • Memory is fading: influence of past samples is reduced exponentially with time n

  17. yk(n) wxj(0) 0 1 2 3 4 n yk(n) wxj(0) 0 1 2 3 4 n FEEDBACK • w = 1 • System is linearly divergent • w > 1 • System is exponentially divergent w = 1 w > 1

  18. input layer of source nodes output layer of neurons NETWORK ARCHITECTURES • Single-Layered Feedforward Networks • Neurons are organized in layers • “Single-layer” refers to output neurons • Source nodes supply to output neurons but not vice versa • Network is feedforward or acyclic

  19. Layer of output neurons Layer of hidden neurons Input layer of source nodes NETWORK ARCHITECTURES • One or more hidden layers • Hidden neurons enable extractions of higher-order statistic • Network acquires global perspective due to extra set of synaptic connections and neural interactions • Multilayer Feedforward Networks • 7-4-2 fully connected network: • 7 source nodes • 4 hidden neurons • 2 output neurons

  20. z-1 z-1 Outputs z-1 z-1 Unit-delay operators Inputs NETWORK ARCHITECTURES • Recurrent Networks • At least one feedback loop • Feedback loops affect learning capability and performance of the network

  21. KNOWLEDGE REPRESENTATION • Definition of Knowledge: • Knowledge refers to stored information or models used by a person or a machine to interpret, predict, and appropriately respond to the outside world • Issues: • What information is actually made explicit • How information is physically encoded for subsequent use • Knowledge representation is goal-directed • Good solution depends on good representation of knowledge

  22. KNOWLEDGE REPRESENTATION • Challenges faced by Neural Networks: • Learn the model of the world/environment • Maintain the model to be consistent with the real world to achieve the goals desired • Neural Networks may learn from a set of observations data in form of input-output pairs (training data/training sample) • Input is input signal and output is the corresponding desired response

  23. KNOWLEDGE REPRESENTATION • Handwritten digit recognition problem • Input signal: one of 10 images of digits • Goal: to identify image presented to the network as input • Design steps: • Select the appropriate architecture • Train the network with subset of examples (learning phase) • Test the network with presentation of data/digit image not seen before, then compare response of network with actual identity of the digit image presented (generalization phase)

  24. KNOWLEDGE REPRESENTATION • Difference with classical pattern-classifier: • Classical pattern-classifier design steps: • Formulate mathematical model of the problem • Validate model with real data • Build based on model • Neural Network design is: • Based on real life data • Data may “speak for itself” • Neural network not only provides model of the environment but also process the information

  25. ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS • AI systems must be able to: • Store knowledge • Use stored knowledge to solve problem • Acquire new knowledge through experience • AI components: • Representation • Knowledge is presented in a language of symbolic structures • Symbolic representation makes it relatively easy for human users

  26. ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS • Reasoning • Able to express and solve broad range of problems • Able to make explicit and implicit information known to it • Have a control mechanism to determine which operation for a particular problem, when a solution is obtained, or when further work on the problem must be terminated • Rules, Data, and Control: • Rules operate on Data • Control operate on Rules • The Travelling Salesman Problem: • Data: possible tours and cost • Rules: ways to go from city to city • Control: which Rules to apply and when

  27. ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS • Learning • Inductive learning: determine rules from raw data and experience • Deductive learning:use rules to determine specific facts Performance element Knowlegdge Base Learning element Environment

  28. ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS

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