327-352. Introduction to Artificial Neural Network (ANN) Methods: ... intensive use
among chemists are the artificial neural networks (or ANNs for short). ______.
Ben Krose ..... notably Teuvo Kohonen, Stephen Grossberg, James Anderson, and .... Examples of recurrent networks have been presented by Anderson.
May 27, 2002 - Transforms inputs into outputs to the best of its ability. Fun dam e ntals. C lass es .... Typically many epochs are required to train the neural network. Fun dam e ntals ..... Using Gray code instead of binary did not improve results.
granted to use this book for non-commercial courses, provided the authors are noti ... 2.3 Training of arti cial neural networks . ..... 11 Dedicated Neuro-Hardware.
used as a textbook for undergraduate and graduate courses, which address the subject of ... part of the book reflect the potential applicability of neural networks to the solution of problems ..... Hardware Implementation Aspects ....................
In this note we provide an overview of the key concepts that have led to the emergence of Artificial Neural Networks as a major paradigm for Data.
Oct 23, 2014 - The neural computer adapts itself during a training period, based on examples of ... Automotive ..... input patterns into a finite number of classes.
pre-formulation parameters for predicting physicochemical properties of drug substances. It also finds its ... neural networks in detail and its applications in the pharmaceutical field. Review and ... work is the sigmoid work. Dynamic ..... For exam
Introduction to Artificial Neural Networks. • What is an Artificial Neural Network ? -
It is a computational system inspired by the. Structure. Processing Method.
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Introduction to Artificial Neural Networks • What is an Artificial Neural Network ? - It is a computational system inspired by the Structure Processing Method Learning Ability of a biological brain - Characteristics of Artificial Neural Networks A large number of very simple processing neuron-lik e processing elements A large number of weighted connections between the elements Distributed representation of knowledge over the connections Knowledge is acquired by network through a learning process
• Elements of Artificial Neural Networks - Processing Units - Topology - Learning Algorithm
• Processing Units
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-3Node input: net i = Σ w ij I i j
Node Output: Oi = f (net i )
• Activation Function
- An example
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• Topology
• Learning - Learn the connection weights from a set of training examples - Different network architectures required different learning algorithms Supervised Learning The network is provided with a correct answer (output) for every input pattern Weights are determined to allow the network to produce answers as close as possible to the known correct answers The back-propagation algorithm belongs into this category
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-6Unsupervised Learning Does not require a correct answer associated with each input pattern in the training set Explores the underlying structure in the data, or correlations between patterns in the data, and organizes patterns into categories from these correlations The Kohonen algorithm belongs into this category Hybrid Learning Comnines supervised and unsupervised learning Part of the weights are determined through supervised learning and the others are obtained through aunsupervised learning
• Computational Properties A single hidden layer feed-forward network with arbitrary sigmoid hidden layer activation functions can approximate arbitrarily well an arbitrary mapping from one finite dimensional space to another
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• Practical Issues - Generalization vs Memorization
Good fit
Bad fit
How to choose the network size (free parameters) How many training examples When to stop training
• Applications - Pattern Classification - Clustering/Categorization - Function approximation - Prediction/Forecasting - Optimization - Content-addressable Memory - Control
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• Two Successful Applications - Zipcode Recognition
