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Neural Networks

This presentation guide you through Neural Networks, use neural networksNeural Networks v/s Conventional<br>Computer, Inspiration from Neurobiology, Types of neural network, The Learning Process, Hetero-association recall mechanisms and Key Features.<br><br>For more topics stay tuned with Learnbay.

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Neural Networks

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  1. Neural Networks Swipe

  2. What is neural network? An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by biological nervous systems. It is composed of a large number of highly interconnected processing elements called neurons. An ANN is configured for a specific application, such as pattern recognition or data classification .

  3. Why use neural networks? Ability to derive meaning from complicated or imprecise data. Extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Adaptive learning. Real Time Operation. Neural networks enable us to find solution where algorithmic methods intensive or do not exist. There is no need to program neural networks they learn with examples. Neural networks offer advantage over conventional techniques. are computationally significant speed

  4. Neural Networks v/s Conventional Computer Conventional approach, but neural networks works similar to human brain and learns by example. computers use an algorithmic

  5. Inspiration from Neurobiology A neuron: many-inputs / one?output unit. Output can be excited or not excited. Incoming signals from other neurons determine if the neuron shall excite ("fire"). Output subject to attenuation in the synapses, which are junction parts of the neuron. output inputs

  6. Types of neural networ Fixed networks, in which the weights cannot be changed, ie dW/dt=0. In such networks, the weights are fixed a priori according to the problem to solve. Adaptive networks, which are able to change their weights, ie dW/dt not= 0.

  7. The Learning Process Associative mapping in which the network learns to produce a particular pattern on the set of input units whenever another particular pattern is applied on the set of input units. The associative mapping can generally be broken down into two mechanisms: Nearest-neighbour recall. Interpolative recall

  8. Hetero-association recall mechanisms Nearest-neighbour recall, where the output pattern produced corresponds to the input pattern stored, which is closest to the pattern presented. Interpolative recall, where the output pattern is a similarity dependent interpolation of the patterns stored corresponding to the pattern presented. Yet another paradigm, which is a variant associative mapping is classification, ie when there is a fixed set of categories into which the input patterns are to be classified.

  9. Key Features Neural network design, training, and simulation. Pattern recognition, clustering, and data-fitting tools. Unsupervised networks including self-organizing maps and competitive layersSupervised feedforward, radial basis, LVQ, time delay, nonlinear autoregressive (NARX), and layer-recurrents. Preprocessing and postprocessing for improving the efficiency of network training and assessing network performance. Modular network representation for managing and visualizing networks of arbitrary size. Routines for improving overfitting. Simulinkblocks for building networks, and advanced blocks for control systems applications. networks including generalization to prevent and evaluating neural

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