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Artificial Neural Network

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Prakash K

Basic definitions, terminologies, and Working of ANN has been explained. This ppt also shows how ANN can be performed in matlab. This material contains the explanation of Feed forward back propagation algorithm in detail.

1 of 58
Artificial Neural Networks Presented By: Ezhilan A K Prakash K PG Scholars – Industrial Engineering, PSG College of Technology.
HISTORY 1943 − The concept of neural network started with the work of physiologist, Warren McCulloch, and mathematician, Walter Pitts, when in 1943 they modelled a simple neural network using electrical circuits in order to describe how neurons in the brain might work. 1949 − Donald Hebb’s book, The Organization of Behaviour, put forth the fact that repeated activation of one neuron by another increases its strength each time they are used. 1958 − A learning method for McCulloch and Pitts neuron model named Perceptron was invented by Rosenblatt. 1960 − Bernard Widrow and Marcian Hoff developed models called "ADALINE" and “MADALINE.” 1961 − Rosenblatt made an unsuccessful attempt but proposed the “backpropagation” scheme for multilayer networks. 1969 − Multilayer perceptron (MLP) was invented by Minsky and Papert.
INTRODUCTION • Neural networks (NN) are parallel computing (many calculations or the execution of processes are carried out concurrently) • Which is basically an attempt to make a computer model of the brain. • It works like a Neuron of a Human Brain. • The main objective is to develop a system to perform various computational tasks faster than the traditional systems. • These tasks include pattern recognition and classification, approximation, optimization, and data clustering.
BIOLOGICAL NEURAL NETWORK • The Basic unit of Nervous system is Neuron (Nerve cell). • Neuron is a special biological cell that processes information. • Human Brain contains huge number of neurons, approximately 1011 with numerous interconnections.
NEURON-SCHEMATIC DIAGRAM
NEURON-ANATOMY • Dendrites − They are tree-like branches, responsible for receiving the information from other neurons it is connected to or in other sense, we can say that they are like the ears of neuron. • Soma − It is the cell body of the neuron and is responsible for processing of information, they have received from dendrites. • Axon − It is like a cable through which neurons send the information. • Synapses − It is the connection between the axon and other neuron dendrites.
ARTIFICIAL NEURAL NETWORK • Artificial Neural Network (ANN) is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks. • ANN acquires a large collection of units that are interconnected in some pattern to allow communication between the units. • These units, also referred to as Nodes or Neurons.
ANN Vs BNN Biological Neural Network Artificial Neural Network Soma Node Dendrite Input Synapse Weight or Interconnections Axon Output
Comparison Between ANN and BNN CRITERIA BNN ANN Processing Massively parallel, slow but superior than ANN Massively parallel, fast but inferior than BNN Size 1011 neurons and 1015 interconnections 102 to 104 nodes mainly depends on the type of application in a network designer Learning They can tolerate ambiguity Very precise, structured and formatted data is required to tolerate ambiguity Fault tolerance Performance degrades with even partial damage It is capable of robust performance, hence has the potential to be fault tolerant Storage capacity Stores the information in the synapse Stores the information in continuous memory locations
Start Initialize Input Define and format Input data Divide data into 3 sets (Training, Testing, Validation) Create Network Layers Train the Network Make Total Error = 0 Last pattern trained Apply the Pattern and Train Total Error < Final Target Error Get Error for each output Neuron and add to Total Error Simulate Network End YES NO YES NO F L O W C H A R T
ARTIFICIAL NEURON-REPRESENTATION X1 W1 X2 X3 Xn W2 W3 Wn x1, x2,……,xn – Input to the artificial neuron w1,w2,….,wn – Weights attached to the input links ∑ - Summation unit – Thresholding Function y - Output Summation Unit Thresholding Unit
Contd… • x1, x2, x3, .... xn are the n inputs to the artificial neuron w1, w2, …. wn, are the weights attached to the input links. • In general we know that biological neuron receives all inputs through dendrites, sums them and produce an output if sum is greater than threshold value. • The input signals are passed on to the cell body(SOMA), through the SYNAPSE which may accelerate or retard an arriving signal.
Contd… • Effective synapse which transmits a stronger signal will have a correspondingly larger weight while a weak synapse will have smaller weights. Thus weights are multiplicative factors of the inputs to account for the strength of the synapse. • Total Input (I) = w1x1 + w2x2 +….…+ wnxn = 𝑖=1 𝑛 𝑤𝑖 𝑥𝑖
Contd… • To generate final output (y), the sum is passed on to a non-linear filter ∅ called Activation function, or Transfer function or squash function which releases the output. y= ∅ (I) • Activation functions used in ANN • Thresholding function (Binary) • Signum function(Bi-Polar) • Sigmoidal function ( Both Binary and Bi-Polar) • In Thresholding function, the sum is compared with a threshold value 𝜃, If the value of I is greater than 𝜃, then the output is 1 else it is 0. • In Signum function, the sum is compared with a threshold value 𝜃, If the value of I is greater than 𝜃, then the output is 1 , if I is lesser or equivalent to 𝜃 it is -1.
Contd… Step(x) = 1 if x ≥ t, else 0 threshold=t Sign(x) = +1 if x ≥ 0, else –1 Sigmoid(x) = 1/(1+e-Øx)
Human EYE Perceptron
Early Neural Network Architecture 1. Rosenblatt’s Perceptron The perceptron is a computational model of the retina of the eye and hence, is named ‘perceptron’. The network comprises three units, the 1. Sensory unit (S) 2. Association unit (A) 3. Response Unit (R)
Rosenblatt’s Perceptron Output Photo detectors Sensory Unit (S) Output Association unit (A) Response Unit (R) Fixed weights Adjustable weights
Contd… • The Sensory Unit (S) receives an input and provides an electric signal as output ( ‘0’ or ‘1’ – Binary form). • The Sensory Unit is randomly connected to the Association Unit (A). • The Association Unit predicts the output of ‘S’ Unit. • The ‘R’ Unit comprises Perceptron, which receives the results of predicates in binary form.
2. ADALINE Network • The Adaptive Linear Neural Element Network formed by Bernard Widrow of Stanford University. • This Network uses Supervised Learning. • There is only one output neuron and the output values are bipolar(-1 or +1) • Input can be Binary or Bipolar . • If the weighted sum of inputs is greater than or equal to 0, then the output is 1 otherwise -1.
X1 W1 X2 X3 Xn W2 W3 Wn x1, x2,……,xn – Input to the artificial neuron w1,w2,….,wn – Weights attached to the input links ∑ - Summation unit – Thresholding Function y - Output A simple ADALINE network Summation Unit Thresholding Unit
3. MADALINE Network • MADALINE is created by combining a number of ADALINES. • This network consist of many layers. • This Network uses Supervised Learning Adaline Adaline Adaline
Limitation of Perceptron • Perceptron cannot handle, in particular, task which are linearly inseparable. • Set of points in two dimensional spaces are linearly separable if the sets can be separated by straight line. • So we go for Back propagation training.
Linearly Separable Vs Linearly Inseparable
Modern Neural Network Architectures The modern neural network architecture are as follows 1. Single Layer Feedforward Network 2. Multilayer Feedforward Network 3. Recurrent Networks
Single Layer Feedforward Network • Single Layer Feedforward Network consist of two layers, namely input and the output layer. • The input layer neurons receives the input signals and the output layer neurons receives the output signals • The synaptic links will carry the weights from input to output, but not vice versa. • These networks are acyclic in nature.
Contd… Input Layer Output Layer
Multilayer Feedforward Network • Multilayer Feedforward Network contains multiple layers. • This also contains input and the output layers, in addition to that I have intermediate layers called Hidden layers. • The hidden layer performs computations before directing the input to the output. • One hidden layer is sufficient for the large majority of problems. • The optimal size of the hidden layer is usually between the size of the input and size of the output layers.
Contd…
Recurrent Network • Recurrent Network differs from other architecture in the sense that it contains at least one feedback loop. • In this the output of a neuron is feedback to the input neuron or the hidden neuron.
ARTIFICIAL NEURAL NETWORK LEARNING METHODS (TRAINING METHODS)
ANN Algorithm Learning Methods Supervised Learning Unsupervised Learning Reinforced Learning Error Correction Gradient Descent Stochastic Least Mean Square Back Propagation Hebbian Competitive Learning Methods
• Every input pattern that is used to train the network is associated with an output pattern. • A teacher is available to indicate whether a system is performing correctly, or to indicate the amount of error in system performance. • Comparison is made with computed output and the correct expected output, to determine the error. 1. Supervised learning
2.Unsupervised Learning • This is Learning by doing. • The target output is not presented to the network. It is as if there is no teacher to present the desired patterns. • The system learns of its own by discovering and adapting to structural features in the input patterns.
3.Reinforced learning • A teacher is available. • He/she Does not present the expected answer. • But indicates if the computed output is correct or incorrect. • This indication provided helps the network in its learning process.
Training Cycle Vs Error
Training – Validation – Test
A Simple Neural Network
Backpropagation • The backpropagation algorithm (Rumelhart and McClelland, 1986) is used in layered feed-forward Artificial Neural Networks. • Back propagation is a multi-layer feed forward, supervised learning network based on gradient descent learning rule. • We provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error (difference between actual and expected results) is calculated. • The idea of the backpropagation algorithm is to reduce this error, until the Artificial Neural Network learns the training data.
Backpropagation Preparation • Training Set A collection of input-output patterns that are used to train the network • Testing Set A collection of input-output patterns that are used to assess network performance • Learning Rate-η A scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments
Backpropagation Algorithm • Randomly choose the initial weights • The activation function of the artificial neurons in ANNs implementing the backpropagation algorithm is a weighted sum (the sum of the inputs xi multiplied by their respective weights wji). • The most common output function is the sigmoidal and is calculated. • Since the error is the difference between the actual and the desired output, the error depends on the weights, and we need to adjust the weights in order to minimize the error. • The backpropagation algorithm now calculates how the error depends on the output, inputs, and weights.
Contd… • We need to calculate how much the error depends on the output. • Calculate the adjusted weight. • If we want to adjust vik, the weights (let’s call them vik ) of a previous layer, we need first to calculate how the error depends not on the weight, but in the input from the previous layer. • Proceed till we get minimum error. • Once we get the minimum error, end the iteration.
Characteristics of Neural Network • The Neural Network exhibit mapping capabilities, that is, they can map inputs patterns to their associated output patterns. • The Neural Network learn by examples. • NN Architecture can be trained with known examples of a problem before they are tested for their inference capability on unknown instances of the problem. • They can identify new objects previously untrained. • They can predict new outcomes from past trends. • Neural Networks are Robust – Fault tolerant .
MATLAB Input Data Matrix
MATLAB Output Data Matrix
Input and Output Data Importing S t e p 1
S t e p 2 Dividing the Data for Training, Validation, Testing
S t e p 3 Defining Hidden Neuron
Training the Network S t e p 4
Making Target Error = 0 S t e p 4
Artificial Neural Network
Operating Characteristics
Advantages of Artificial Neural Network 1. A neural network can perform tasks that a linear program can not. 2. When an element of the neural network fails, it can continue without any problem by their parallel nature. 3. A neural network learns and does not need to be reprogrammed. 4. It can be implemented in any application. 5. It can be implemented without any problem.
Disadvantages of Artificial Neural Network 1. The neural network needs training to operate 2. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. 3. Requires high processing time for large neural networks.
Applications • Signal processing: suppress line noise, with adaptive echo canceling, blind source separation. • Siemens successfully uses neural networks for process automation in basic industries, e.g., in rolling mill control more than 100 neural networks do their job, 24 hours a day. • Robotics - navigation, vision recognition. • Pattern recognition, i.e. recognizing handwritten characters, e.g. the current version of Apple's Newton uses a neural net. • Medicine, storing medical records based on case information. • Speech production: reading text aloud (NETtalk). • Vision: face recognition , edge detection, visual search engines. • Financial Applications: Time series analysis, Stock market prediction. • Data Compression: speech signal, image, e.g. faces.
A Glimpse on Neural Network • NN is interconnected network that resembles human brain. • The most important characteristic of NN is its ability to learn. When presented with training set (form of supervised learning) where input and output values are known. • NN model could be created to help with classifying new data. • Results that are achieved by using NN are encouraging, especially in some fields like pattern recognition.
References: • Neural Networks, Fuzzy Systems, and Evolutionary Algorithms Synthesis and Application by S.Rajasekaran, G.A.Vijayalakshmi Pai. • Introduction to Neural Networks – Open Course Ware, Massachusetts Institute of Technology. • Back Propagation Neural Network - Dr K Vijayarekha • Mahesh B. Parappagoudar, D.K. Pratihar , G.L. Datta , Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing 8 (2008) 239–260. • Marek Vrabea, Ildiko Mankovaa, Jozef Benoa, Jaroslav Tuharskýa, Surface roughness prediction using artificial neural networks when drilling Udimet 720. Procedia Engineering 48 ( 2012 ) 693 – 700.
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Artificial Neural Network

  • 1. Artificial Neural Networks Presented By: Ezhilan A K Prakash K PG Scholars – Industrial Engineering, PSG College of Technology.
  • 2. HISTORY 1943 − The concept of neural network started with the work of physiologist, Warren McCulloch, and mathematician, Walter Pitts, when in 1943 they modelled a simple neural network using electrical circuits in order to describe how neurons in the brain might work. 1949 − Donald Hebb’s book, The Organization of Behaviour, put forth the fact that repeated activation of one neuron by another increases its strength each time they are used. 1958 − A learning method for McCulloch and Pitts neuron model named Perceptron was invented by Rosenblatt. 1960 − Bernard Widrow and Marcian Hoff developed models called "ADALINE" and “MADALINE.” 1961 − Rosenblatt made an unsuccessful attempt but proposed the “backpropagation” scheme for multilayer networks. 1969 − Multilayer perceptron (MLP) was invented by Minsky and Papert.
  • 3. INTRODUCTION • Neural networks (NN) are parallel computing (many calculations or the execution of processes are carried out concurrently) • Which is basically an attempt to make a computer model of the brain. • It works like a Neuron of a Human Brain. • The main objective is to develop a system to perform various computational tasks faster than the traditional systems. • These tasks include pattern recognition and classification, approximation, optimization, and data clustering.
  • 4. BIOLOGICAL NEURAL NETWORK • The Basic unit of Nervous system is Neuron (Nerve cell). • Neuron is a special biological cell that processes information. • Human Brain contains huge number of neurons, approximately 1011 with numerous interconnections.
  • 6. NEURON-ANATOMY • Dendrites − They are tree-like branches, responsible for receiving the information from other neurons it is connected to or in other sense, we can say that they are like the ears of neuron. • Soma − It is the cell body of the neuron and is responsible for processing of information, they have received from dendrites. • Axon − It is like a cable through which neurons send the information. • Synapses − It is the connection between the axon and other neuron dendrites.
  • 7. ARTIFICIAL NEURAL NETWORK • Artificial Neural Network (ANN) is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks. • ANN acquires a large collection of units that are interconnected in some pattern to allow communication between the units. • These units, also referred to as Nodes or Neurons.
  • 8. ANN Vs BNN Biological Neural Network Artificial Neural Network Soma Node Dendrite Input Synapse Weight or Interconnections Axon Output
  • 9. Comparison Between ANN and BNN CRITERIA BNN ANN Processing Massively parallel, slow but superior than ANN Massively parallel, fast but inferior than BNN Size 1011 neurons and 1015 interconnections 102 to 104 nodes mainly depends on the type of application in a network designer Learning They can tolerate ambiguity Very precise, structured and formatted data is required to tolerate ambiguity Fault tolerance Performance degrades with even partial damage It is capable of robust performance, hence has the potential to be fault tolerant Storage capacity Stores the information in the synapse Stores the information in continuous memory locations
  • 10. Start Initialize Input Define and format Input data Divide data into 3 sets (Training, Testing, Validation) Create Network Layers Train the Network Make Total Error = 0 Last pattern trained Apply the Pattern and Train Total Error < Final Target Error Get Error for each output Neuron and add to Total Error Simulate Network End YES NO YES NO F L O W C H A R T
  • 11. ARTIFICIAL NEURON-REPRESENTATION X1 W1 X2 X3 Xn W2 W3 Wn x1, x2,……,xn – Input to the artificial neuron w1,w2,….,wn – Weights attached to the input links ∑ - Summation unit – Thresholding Function y - Output Summation Unit Thresholding Unit
  • 12. Contd… • x1, x2, x3, .... xn are the n inputs to the artificial neuron w1, w2, …. wn, are the weights attached to the input links. • In general we know that biological neuron receives all inputs through dendrites, sums them and produce an output if sum is greater than threshold value. • The input signals are passed on to the cell body(SOMA), through the SYNAPSE which may accelerate or retard an arriving signal.
  • 13. Contd… • Effective synapse which transmits a stronger signal will have a correspondingly larger weight while a weak synapse will have smaller weights. Thus weights are multiplicative factors of the inputs to account for the strength of the synapse. • Total Input (I) = w1x1 + w2x2 +….…+ wnxn = 𝑖=1 𝑛 𝑤𝑖 𝑥𝑖
  • 14. Contd… • To generate final output (y), the sum is passed on to a non-linear filter ∅ called Activation function, or Transfer function or squash function which releases the output. y= ∅ (I) • Activation functions used in ANN • Thresholding function (Binary) • Signum function(Bi-Polar) • Sigmoidal function ( Both Binary and Bi-Polar) • In Thresholding function, the sum is compared with a threshold value 𝜃, If the value of I is greater than 𝜃, then the output is 1 else it is 0. • In Signum function, the sum is compared with a threshold value 𝜃, If the value of I is greater than 𝜃, then the output is 1 , if I is lesser or equivalent to 𝜃 it is -1.
  • 15. Contd… Step(x) = 1 if x ≥ t, else 0 threshold=t Sign(x) = +1 if x ≥ 0, else –1 Sigmoid(x) = 1/(1+e-Øx)
  • 17. Early Neural Network Architecture 1. Rosenblatt’s Perceptron The perceptron is a computational model of the retina of the eye and hence, is named ‘perceptron’. The network comprises three units, the 1. Sensory unit (S) 2. Association unit (A) 3. Response Unit (R)
  • 18. Rosenblatt’s Perceptron Output Photo detectors Sensory Unit (S) Output Association unit (A) Response Unit (R) Fixed weights Adjustable weights
  • 19. Contd… • The Sensory Unit (S) receives an input and provides an electric signal as output ( ‘0’ or ‘1’ – Binary form). • The Sensory Unit is randomly connected to the Association Unit (A). • The Association Unit predicts the output of ‘S’ Unit. • The ‘R’ Unit comprises Perceptron, which receives the results of predicates in binary form.
  • 20. 2. ADALINE Network • The Adaptive Linear Neural Element Network formed by Bernard Widrow of Stanford University. • This Network uses Supervised Learning. • There is only one output neuron and the output values are bipolar(-1 or +1) • Input can be Binary or Bipolar . • If the weighted sum of inputs is greater than or equal to 0, then the output is 1 otherwise -1.
  • 21. X1 W1 X2 X3 Xn W2 W3 Wn x1, x2,……,xn – Input to the artificial neuron w1,w2,….,wn – Weights attached to the input links ∑ - Summation unit – Thresholding Function y - Output A simple ADALINE network Summation Unit Thresholding Unit
  • 22. 3. MADALINE Network • MADALINE is created by combining a number of ADALINES. • This network consist of many layers. • This Network uses Supervised Learning Adaline Adaline Adaline
  • 23. Limitation of Perceptron • Perceptron cannot handle, in particular, task which are linearly inseparable. • Set of points in two dimensional spaces are linearly separable if the sets can be separated by straight line. • So we go for Back propagation training.
  • 24. Linearly Separable Vs Linearly Inseparable
  • 25. Modern Neural Network Architectures The modern neural network architecture are as follows 1. Single Layer Feedforward Network 2. Multilayer Feedforward Network 3. Recurrent Networks
  • 26. Single Layer Feedforward Network • Single Layer Feedforward Network consist of two layers, namely input and the output layer. • The input layer neurons receives the input signals and the output layer neurons receives the output signals • The synaptic links will carry the weights from input to output, but not vice versa. • These networks are acyclic in nature.
  • 28. Multilayer Feedforward Network • Multilayer Feedforward Network contains multiple layers. • This also contains input and the output layers, in addition to that I have intermediate layers called Hidden layers. • The hidden layer performs computations before directing the input to the output. • One hidden layer is sufficient for the large majority of problems. • The optimal size of the hidden layer is usually between the size of the input and size of the output layers.
  • 30. Recurrent Network • Recurrent Network differs from other architecture in the sense that it contains at least one feedback loop. • In this the output of a neuron is feedback to the input neuron or the hidden neuron.
  • 31. ARTIFICIAL NEURAL NETWORK LEARNING METHODS (TRAINING METHODS)
  • 32. ANN Algorithm Learning Methods Supervised Learning Unsupervised Learning Reinforced Learning Error Correction Gradient Descent Stochastic Least Mean Square Back Propagation Hebbian Competitive Learning Methods
  • 33. • Every input pattern that is used to train the network is associated with an output pattern. • A teacher is available to indicate whether a system is performing correctly, or to indicate the amount of error in system performance. • Comparison is made with computed output and the correct expected output, to determine the error. 1. Supervised learning
  • 34. 2.Unsupervised Learning • This is Learning by doing. • The target output is not presented to the network. It is as if there is no teacher to present the desired patterns. • The system learns of its own by discovering and adapting to structural features in the input patterns.
  • 35. 3.Reinforced learning • A teacher is available. • He/she Does not present the expected answer. • But indicates if the computed output is correct or incorrect. • This indication provided helps the network in its learning process.
  • 38. A Simple Neural Network
  • 39. Backpropagation • The backpropagation algorithm (Rumelhart and McClelland, 1986) is used in layered feed-forward Artificial Neural Networks. • Back propagation is a multi-layer feed forward, supervised learning network based on gradient descent learning rule. • We provide the algorithm with examples of the inputs and outputs we want the network to compute, and then the error (difference between actual and expected results) is calculated. • The idea of the backpropagation algorithm is to reduce this error, until the Artificial Neural Network learns the training data.
  • 40. Backpropagation Preparation • Training Set A collection of input-output patterns that are used to train the network • Testing Set A collection of input-output patterns that are used to assess network performance • Learning Rate-η A scalar parameter, analogous to step size in numerical integration, used to set the rate of adjustments
  • 41. Backpropagation Algorithm • Randomly choose the initial weights • The activation function of the artificial neurons in ANNs implementing the backpropagation algorithm is a weighted sum (the sum of the inputs xi multiplied by their respective weights wji). • The most common output function is the sigmoidal and is calculated. • Since the error is the difference between the actual and the desired output, the error depends on the weights, and we need to adjust the weights in order to minimize the error. • The backpropagation algorithm now calculates how the error depends on the output, inputs, and weights.
  • 42. Contd… • We need to calculate how much the error depends on the output. • Calculate the adjusted weight. • If we want to adjust vik, the weights (let’s call them vik ) of a previous layer, we need first to calculate how the error depends not on the weight, but in the input from the previous layer. • Proceed till we get minimum error. • Once we get the minimum error, end the iteration.
  • 43. Characteristics of Neural Network • The Neural Network exhibit mapping capabilities, that is, they can map inputs patterns to their associated output patterns. • The Neural Network learn by examples. • NN Architecture can be trained with known examples of a problem before they are tested for their inference capability on unknown instances of the problem. • They can identify new objects previously untrained. • They can predict new outcomes from past trends. • Neural Networks are Robust – Fault tolerant .
  • 46. Input and Output Data Importing S t e p 1
  • 47. S t e p 2 Dividing the Data for Training, Validation, Testing
  • 50. Making Target Error = 0 S t e p 4
  • 53. Advantages of Artificial Neural Network 1. A neural network can perform tasks that a linear program can not. 2. When an element of the neural network fails, it can continue without any problem by their parallel nature. 3. A neural network learns and does not need to be reprogrammed. 4. It can be implemented in any application. 5. It can be implemented without any problem.
  • 54. Disadvantages of Artificial Neural Network 1. The neural network needs training to operate 2. The architecture of a neural network is different from the architecture of microprocessors therefore needs to be emulated. 3. Requires high processing time for large neural networks.
  • 55. Applications • Signal processing: suppress line noise, with adaptive echo canceling, blind source separation. • Siemens successfully uses neural networks for process automation in basic industries, e.g., in rolling mill control more than 100 neural networks do their job, 24 hours a day. • Robotics - navigation, vision recognition. • Pattern recognition, i.e. recognizing handwritten characters, e.g. the current version of Apple's Newton uses a neural net. • Medicine, storing medical records based on case information. • Speech production: reading text aloud (NETtalk). • Vision: face recognition , edge detection, visual search engines. • Financial Applications: Time series analysis, Stock market prediction. • Data Compression: speech signal, image, e.g. faces.
  • 56. A Glimpse on Neural Network • NN is interconnected network that resembles human brain. • The most important characteristic of NN is its ability to learn. When presented with training set (form of supervised learning) where input and output values are known. • NN model could be created to help with classifying new data. • Results that are achieved by using NN are encouraging, especially in some fields like pattern recognition.
  • 57. References: • Neural Networks, Fuzzy Systems, and Evolutionary Algorithms Synthesis and Application by S.Rajasekaran, G.A.Vijayalakshmi Pai. • Introduction to Neural Networks – Open Course Ware, Massachusetts Institute of Technology. • Back Propagation Neural Network - Dr K Vijayarekha • Mahesh B. Parappagoudar, D.K. Pratihar , G.L. Datta , Forward and reverse mappings in green sand mould system using neural networks. Applied Soft Computing 8 (2008) 239–260. • Marek Vrabea, Ildiko Mankovaa, Jozef Benoa, Jaroslav Tuharskýa, Surface roughness prediction using artificial neural networks when drilling Udimet 720. Procedia Engineering 48 ( 2012 ) 693 – 700.