Evolving Neural Networks

Jul 22, 2013

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Adaptation. There are two forms of adaptationLearningTraining on a set of examples.Fitting your behavior to training data.Minimizing error.EvolutionPopulation based search.Random MutationReproductionFitness Selection. Combining The Two. Combining the strategies of evolutionary algorithms wi

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Evolving Neural Networks

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Neural Networks

Neural Networks. References. Neural network design, M. T. Hagan & H. B. Demuth & M. Beale PWS publishing company – 1996. Neural network: A comprehensive foundation, Simon Haykin, Prentice Hall – 1999.

1.01k views • 26 slides

Evolving Networks

Evolving Networks Graphs where edges may appear or disappear. Why evolving graphs?Applications?What simplifying models are available?Assumptions? What are practical problems in applying these models?What can we infer?. Acknowledgments Des Higham, Strathclyde, and PG, Reading Funded via EPSRC

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Neural Networks

Neural Networks. Outline: The biological neuron History of neural networks research The Perceptron Examples Training algorithm Fundamental training theorem Two-level perceptrons 2-Level feedforward neural nets with sigmoid activation functions Backpropagation and the Delta rule.

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Neural Networks

Neural Networks. CIS 488/588 Bruce R. Maxim UM-Dearborn. Neural Networks. Can be thought of as arithmetic constraint networks Tend to be designed in layers Input layer Hidden layers (1 or more) Output layer. Neural Network Types. Feed-forward

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Neural Networks

Neural Networks. biological neuron. artificial neuron. A two-layer neural network. Output layer (activation represents classification). Weighted connections. Hidden layer (“internal representation”). Input layer (activations represent feature vector for one training example).

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NEURAL NETWORKS

NEURAL NETWORKS. & Fuzzy logic. Neural network is dubbed as an replica of a human brain on the lines of its working model The functionality of neural network can be equated to the functions of an human brain How does human brain learn

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Neural Networks

Neural Networks. Background. - Neural Networks can be : - Biological models - Artificial models - Desire to produce artificial systems capable of sophisticated computations similar to the human brain. Biological analogy and some main ideas.

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Neural Networks

Neural Networks. Instructor: Dr. Abdul Basit Lecture No. 1. Course Contents. Introduction and Review Learning Processes Single & Multi-layer Perceptrons Radial Basis Function Networks Support Vector and Committee Machines Principle Component Analysis (PCA) Self Organizing Maps (SOMs)

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Neural Networks

Neural Networks. Dr. Thompson March 19, 2013. Artificial Intelligence. Robotics Computer Vision & Speech Recognition Expert Systems Pattern Recognition Machine Learning Natural Language Processing Prognostics & Diagnostics. Neural Network Applications. Character Recognition

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Neural Networks

Neural Networks. MSE 2400 EaLiCaRA Spring 2014 Dr. Tom Way. Background. Neural Networks can be : - Biological models - Artificial models Desire to produce artificial systems capable of sophisticated computations similar to the human brain .

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Neural Networks

Neural Networks . EA C461 - Artificial Intelligence. Topics. Connectionist Approach to Learning Perceptron, Perceptron Learning. Neural Net example: ALVINN. Autonomous vehicle controlled by Artificial Neural Network Drives up to 70mph on public highways.

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Neural Networks

Neural Networks. ICS 273A UC Irvine Instructor: Max Welling. Neurons. Neurons communicate by receiving signals on their dendrites. Adding these signals and firing off a new signal along the axon if the total input exceeds a threshold. The axon connects to new dendrites through

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Neural Networks

Neural Networks. Marcel Jiřina. Introduction. Neural networks and their use to classification and other tasks ICS AS CR Theoretical computer science Neural networks , genetic alg. and n onlinear methods Numeric algorithms .. 1 mil. eq. Fuzzy sets, approximate reasoning, possibility th.

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Neural networks

Neural networks. Neural networks. Neural networks are made up of many artificial neurons. Each input into the neuron has its own weight associated with it illustrated by the red circle.

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Evolving Neural Networks in Classification

Evolving Neural Networks in Classification. Sunghwan Sohn. Education. Ph.D. Candidate, Engineering Management M.S., Computer Engineering B.E., Electronics Engineering. Experience. Research Assistant in Smart Engineering Systems Lab Developing Evolving Neural Networks in Data Mining

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Neural Networks

Neural Networks. and. Pattern Recognition. Giansalvo EXIN Cirrincione. unit #1. Neural network definition. A neural network is a parallel distributed processor with adaptive capabilities (weights or states). nucleus. cell body. axon. dendrites. The neuron. The neuron. The neuron.

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Neural Networks

Neural Networks. L. Manevitz Dept of Computer Science manevitz@cs.haifa.ac.il Tel: 2420. Plan. Requirements Background Brains and Computers Computational Models Learnability vs Programming Representability vs Training Rules Abstractions of Neurons Abstractions of Networks

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Neural Networks

Neural Networks. and. Pattern Recognition. Giansalvo EXIN Cirrincione. Sabine Van Huffel. unit #5. The non-linear function of many variables is represented in terms of compositions of non-linear functions of a single variable, called activation functions. The multi-layer perceptron.

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Neural Networks

Neural Networks. Examples of Single Layer Perceptron Convergence in Linear Case No Convergence in Non-linear Case (Hyper)Line of separation Separation of space by (hyper)line/plane Orthogonality to weight vector. Neural Networks.

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Neural Networks

Neural Networks. Course 2L490 Lecturer: Rudolf Mak E-mail: r.h.mak@tue.nl Course notes: H.M.M. ten Eikelder Neural Networks Webpage: Neurale Netwerken (2L490). Today’s topics. BNNs versus ANNs computing power and future development BNNs quick overview ANNs

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Evolving Neural Networks

Evolving Neural Networks. Learning and Evolution: Their secret conspiracy to take over the world. Adaptation. There are two forms of adaptation Learning Training on a set of examples. Fitting your behavior to training data. Minimizing error. Evolution Population based search.

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