5_chapter.ppt - BEU 5173 ARTIFICIAL INTELLIGENCE CHAPTER 5 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS(ANN W.R.W OMAR JKE Neural Networks NN 1 1 Course

5_chapter.ppt - BEU 5173 ARTIFICIAL INTELLIGENCE CHAPTER 5...

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Neural Networks NN 1 1 CHAPTER 5 INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS (ANN) BEU 5173 ARTIFICIAL INTELLIGENCE W.R.W OMAR, JKE
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Neural Networks NN 1 2 Course Outline 5.1 Introduction and how the brain works 5.2 The neuron as a simple computing element 5.3 The perceptron 5.4 Multilayer neural networks 5.5 Accelerated learning in multilayer neural networks 5.6 The Hopfield network 5.7 Bidirectional associative memory 5.8 Self-organising neural networks
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Neural Networks NN 1 3 5.1 Introduction and how the brain works Artificial Neural Networks are relatively crude electronic models based on the neural structure of the brain. The brain basically learns from experience
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Neural Networks NN 1 4
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Neural Networks NN 1 5
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Neural Networks NN 1 6
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Neural Networks NN 1 7 Human brain is a densely interconnected network of approximately 10 11 neurons, each connected to, on average, 10 4 others. Neuron activity is excited or inhibited through connections to other neurons. The fastest neuron switching times are known to be on the order of 10 -3 sec.
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Neural Networks NN 1 8 The cell itself includes a nucleus (at the center). To the right of cell 2, the dendrites provide input signals to the cell. To the right of cell 1, the axon sends output signals to cell 2 via the axon terminals. These axon terminals merge with the dendrites of cell 2.
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Neural Networks NN 1 9 5.2 The Neuron As A Simple Computing Element Inputs x o x i x n Output, =F (v (t)) Weights . . . W1 Activation, v (t) NEURON W2 Wn Y Y Y Y
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Neural Networks NN 1 10 A NN is a machine learning approach inspired by the way in which the brain performs a particular learning task : Knowledge about the learning task is given in the form of examples . Inter neuron connection strengths ( weights ) are used to store the acquired information (the training examples). During the learning process the weights are modified in order to model the particular learning task correctly on the training examples .
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Application 1. classification in marketing: consumer spending pattern classification In defence: radar and sonar image classification In agriculture & fishing: fruit and catch grading In medicine: ultrasound and electrocardiogram image classification, EEGs, medical diagnosis 2. recognition and identification In general computing and telecommunications: speech, vision and handwriting recognition In finance: signature verification and bank note verification 3. assessment In engineering: product inspection monitoring and control In defence: target tracking In security: motion detection, surveillance image analysis and fingerprint matching 4. forecasting and prediction In finance: foreign exchange rate and stock market forecasting In agriculture: crop yield forecasting Neural Networks NN 1 11
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Neural Networks NN 1 12 Learnings Method
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  • Fall '19
  • Rosemehah Wan Omar

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