Neural Network


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Neural Network

  1. 1. 1 8th Term Neural Networks By Engr. Muhammad Imran
  2. 2. Books: 1. Haykin, S., Neural Networks – A Comprehensive Foundation, Second edition or latest, McMillan. 2. Hagan, M.T., Demuth, H.B., and Beale, M., Neural Network Design, PWS Publishing Company. 2
  3. 3.  Artificial Neural Networks (ANNs) learn by experience rather than by modeling or programming.  ANN architectures are distributed, inherently parallel and potentially real time.  They have the ability to generalize.  They do not require a prior understanding of the process or phenomenon being studied.  They can form arbitrary continuous non-linear mappings.  They are robust to noisy data.  VLSI implementation is easy 3
  4. 4.  Pattern recognition including character recognition, speech recognition, face recognition, on-line signature recognition color recognition etc.  Weather forecasting, load forecasting.  Intelligent routers, intelligent traffic monitoring, intelligent filter design.  Intelligent controller design  Intelligent modeling. and many more 4
  5. 5.  Conventional neural networks are black box models.  Tools for analysis and model validation are not well established.  An intelligent machine can only solve some specific problem for which it is trained.  Human brain is very complex and cannot be fully simulated with present computing power. An artificial neural network does not have capability of human brain.  Issue:  What is the difference between human brain neurons and other animal brain neurons? 5
  6. 6. A universal definition of ANNs is not available, however, the following definition summarizes the basic features of an ANN: Artificial Neural Networks, also called Neurocomputing or Parallel Distributed Processes (PDP) or connectionist networks or simply neural networks are interconnected assemblies of simple processing elements, called neurons, units or nodes, whose functionality is loosely based on the biological neuron. The processing ability of the network is stored in the inter-unit connection strength, or weights, obtained by a process of adaptation to, or learning from a set of training patterns. 6
  7. 7. 7 Asimplifiedviewofabiological(real)neuron
  8. 8. 8 Examples of some real neurons
  9. 9. 9 Image of the vertical organization of neurons in the primary visual cortex
  10. 10. The central part of a neuron is called the soma or cell body which contains the nucleus and the protein synthesis machinery. The size of soma of a typical neuron is about 10 to 80µm. Almost all the logical functions are realized in this part of a neuron. 10
  11. 11. The dendrites represent a highly branching tree of fibers and are attached to the soma. The word dendrite has been taken from the Greek word dendro which means tree. Dendrites connect the neuron to a set of other neurons. Dendrites either receive inputs from other neurons or connect other dendrites to the synaptic outputs. 11
  12. 12. It is a long tubular fiber which divides itself into a number of branches towards its end. Its length can be from 100 µm to 1 m. The function of an axon is to transmit the generated neural activity to other neurons or to muscle fibers. In other words, it is output channel of the neuron. The point where the axon is connected to its cell body is called the Hillock zone. 12
  13. 13. The junction at which a signal is passed from one neuron to the next is called a synapse (from the Greek verb to join). It has a button like shape with diameter around 1 µm. Usually a synapse is not a physical connection (the axon and the dendrite do not touch) but there is a gap called the synaptic cleft that is normally between 200Å to 500Å (1Å = 10-10 m). The strength of synaptic connection between neurons can be chemically altered by the brain in response to favorable and unfavorable stimuli in such a way as to adapt the organism to function optimally within its environment. 13
  14. 14. 14 “The nerve fibre is clearly a signalling mechanism of limited scope. It can only transmit a succession of brief explosive waves, and the message can only be varied by changes in the frequency and in the total number of these waves. … But this limitation is really a small matter, for in the body the nervous units do not act in isolation as they do in our experiments. A sensory stimulus will usually affect a number of receptor organs, and its result will depend on the composite message in many nerve fibres.” Lord Adrian, Nobel Acceptance Speech, 1932.
  15. 15.  Single neurons are highly complex electrochemical devices  Synaptically connected networks are only part of the story  Many forms of interneuron communication now known – acting over many different spatial and temporal scales 15
  16. 16. 16 Single neuron activity • Membrane potential is the voltage difference between a neuron and its surroundings (0 mV) CellCell CellCell 0 Mv Membrane potential
  17. 17. 17 Single neuron activity •If you measure the membrane potential of a neuron and print it out on the screen, it looks like: spike
  18. 18. 18 Single neuron activity •A spike is generated when the membrane potential is greater than its threshold
  19. 19. 19 Abstraction •So we can forget all sub-threshold activity and concentrate on spikes (action potentials), which are the signals sent to other neurons Spikes
  20. 20. 20 • Only spikes are important since other neurons receive them (signals) • Neurons communicate with spikes • Information is coded by spikes • So if we can manage to measure the spiking time, we decipher how the brain works ….
  21. 21. 21 Again its not quite that simple • spiking time in the cortex is random
  22. 22. 22 With identical input for the identical neuron spike patterns are similar, but not identical
  23. 23. 23 Recording from a real neuron: membrane potential
  24. 24. 24 Single spiking time is meaningless To extract useful information, we have to average to obtain the firing rate r  for a group of neurons in a local circuit where neuron codes the same information  over a time window Local circuit = Time window = 1 sec r = = 6 Hz
  25. 25. 25 So we can have a network of these local groups w1: synaptic strength wn r1 rn R f w rj j = ∑( ) Hence we have firing rate of a group of neurons
  26. 26. 26 ri is the firing rate of input local circuit The neurons at output local circuits receive signals in the form The output firing rate of the output local circuit is then given by R where f is the activation function, generally a Sigmoidal function of some sort ∑= N i ii rw 1 )( 1 ∑= = N i ii rwfR wi weight, (synaptic strength) measuring the strength of the interaction between neurons.
  27. 27. 27 Artificial Neural networks Local circuits (average to get firing rates) Single neuron (send out spikes)
  28. 28. No, with present computing power is it almost impossible to simulate a full behavior of human brain. Reasons:  It is estimated that the human cerebral cortex contains 100 billion neurons. Each neuron has as many as 1000 dendrites and, hence within the cerebral cortex there are approximately 100,000 billion synapses.  The behavior of the real nervous system is very complex and not yet fully known. 28
  29. 29. 29 w2 w1 wp ∑ f x1 x2 Activation function xp b y       += ∑= p 1i ii bpwfy
  30. 30. 30 w2 w1 wp ∑ f x1 x2 Activation function xp y w0x0 For simplicity, the threshold contribution b may be treated as an extra input to the neuron, as shown below, where x0 = 1 w0 = b. ( )      = ∑= p 0i iixwfy In this case
  31. 31. The activation function of a neuron, also known as transfer function, may be linear or non-linear. A particular activation function is chosen to satisfy some specification of the problem that the neuron is attempting to solve. A variety of activation functions have been proposed. Some of these are discussed below: 31
  32. 32. (a) The hard limit activation function: This activation function sets the output of the neuron to 0 if the function argument is less than 0, or 1 if its argument is greater than or equal to 0. (see figure below). This function is generally used to create neurons that classify inputs into two distinct classes. 32 0 1
  33. 33. Example 1: The input to a neuron is 3, and its weight is 1.8. (a) What is the net input to the activation function? (b) What is the output of the neuron if it has the hard limit transfer function? Solution: (a) Net input = u = 1.8×3 = 5.4 (b) Neuron output = f(u) = 1. 33
  34. 34. Example 2: Repeat example 1 if its bias is (i) –2 (ii) –6 Solution: (a) net input = u = (1.8×3) + (-2) = 5.4 – 2 = 3.4 output = f(u) = 1.0 (b) net input = u = (1.8×3) + (-6) = 5.4 – 6 = -0.6 output = 0. 34
  35. 35. Example 3: Given a two input neuron with the following parameters: b = 1.4, W = [1.2 3.4] and P = [-3 5], calculate the neuron output for the hard limit transfer function. Solution: net input to the activation function = Neuron output = f(u) = 1.0 35 [ ]=u [ ] 8.144.1 5 3 4.32.1u =+     − =
  36. 36. (b) Symmetrical hard limit: This activation function is defined as follows: y = -1 u < 0 y = +1 u ≥ 0 Where u is the net input and y is the output of the function (see figure below): 36 -1 0 1 y u0
  37. 37. Example 4: Given a two output neuron with the following parameters: b = 1.2, W = [3 2], and p = [-5 6]. Calculate the neuron output for the symmetrical hard limit activation function. Solution: Output = y = f(u) = -1 37 [ ] 8.12.1 6 5 23u −=+     − =
  38. 38. (c) Linear activation function: The output of a linear activation function is equal to its input: y = u as shown in the following figure: 38 0 0 u y
  39. 39. (d) The log-sigmoid activation function: This activation function takes the input (which may have any value between plus and minus infinity) and squashes the output into the range 0 to 1, according to the expression: 39 u e1 1 y − + = A plot of this function is given below: 0 u0 1
  40. 40. Example 5: Repeat example 2 for a log- sigmoid activation function. Solution: (a) net input = u = 3.4 output = f(u) = 1/(1 + e-3.4 ) = 0.9677 (b) net input u = -0.6 output = f(u) = 1/(1 + e0.6 ) = 0.3543 Example 6: Repeat example 3 for a log sigmoid activation function. Solution: output = 1 40
  41. 41. (e) Hyperbolic Tangent Sigmoid: This activation function is shown in the following figure and is defined mathematically as follows: 41 uu uu ee ee y − − + − = 0-1 0 1
  42. 42. 42 0 +1 -1 Saturation LimiterGaussian function Schmitt trigger +1 -1 and many more
  43. 43. RULES FOR SIGNAL FLOW GRAPH  Rule 1: A signal flows along a link only in the direction defined by the arrow on the link. (see fig a: synaptic link, fig b: activation link)  Rule 2: A node signal equals the algebraic sum of all signals entering the pertinent node via the incoming links. (see fig c)  Rule 3: The signal at a node is transmitted to each outgoing link originating from the node, with the transmission being entirely independent of the transfer function of the outgoing links. (see fig d) 43
  44. 44.  The block diagram of a neuron shown in figure, provides a functional description of the various elements that constitute the model of an artificial neuron.  We can simplify the appearance by using signal flow graph. 44
  45. 45.  Feedback is said to exist in a dynamic system whenever the output of an element in the system influences in the input applied to that particular element, thereby giving rise to one or more closed paths for the transmission of signals around the system.  The Open loop architectural graph of a neuron 45
  46. 46. 46
  47. 47. Connecting several neurons in some specific manner yields an artificial neural network.  The architecture of an ANN defines the network structure, that is the number of neurons in the network and their interconnectivity.  In a typical ANN architecture, the artificial neurons are connected in layers and they operate in parallel.  The weights or the strength of connection between the neurons are adapted during use to yield good performance.  Each ANN architecture has its own learning rule. 47
  48. 48. 48 (a) Single Layer feed forward networks: Input layer Output layer
  49. 49. 49 (b) Multilayer Feed forward Networks Input layer Hidden layer Output layer Fullyconnectedfeedforwardnetwork withonehiddenlayer
  50. 50. 50 (b) Multilayer Feedforward NetworksPartiallyconnectedfeedforwardnetwork withonehiddenlayer Input signal Input layer Hidden layer Output layer
  51. 51. 51 Recurrentnetworkwithnohiddenneurons
  52. 52. 52
  53. 53. 53