Applying Neural Networks

Applying Neural Networks : A Practical Guide

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In this computer-based era, neural networks are an invaluable tool. They have been applied extensively in business forecasting, machine health monitoring, process control, and laboratory data analysis due to their modeling capabilities. There are numerous applications for neural networks, but a great deal of care and expertise is necessary to keep a neural-based project in working order. This all-inclusive coverage gives you everything you need to put neural networks into practice. This informative book shows the reader how to plan, run, and benefit from a neural-based project without running into the roadblocks that often crop up. Theauthor uses the most popular type of neural network, the Multi-Layer Perceptron, and presents every step of its development. Each chapter presents a subsequent stage in network development through easy-to-follow discussion. Every decision and possible problem is considered in depth, and solutions are offered. The book includes a how-to-do-it reference section, and a set of worked examples.
The second half of the book examines the sucessful application of neural networks in fields including signal processing, financial prediction, business decision support, and process monitoring and control. The book comes complete with a disk containing C and C++ programs to get you started.
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Product details

  • Mixed media product | 300 pages
  • 153.7 x 228.3 x 20.1mm | 577.27g
  • Morgan Kaufmann Publishers In
  • San Francisco, United States
  • English
  • glossary, bibliography, index
  • 0126791708
  • 9780126791709

Table of contents

Techniques for building neural networks: introduction; what are neural networks? how does neural computing differ from traditional programming? how are neural networks built? how do neural networks learn? what do I need to build an MLP? the neural project life cycle; the generalisation-accuracy trade-off; implementation; details; activation and learning equations; a simple example: modelling a pendulum. Dat encoding and re-coding: introduction; data type classification; initial statistical calculations; dimensionality reduction; scaling a data set; neural encoding methods; temporal data; when to carry out re-coding; implementation; details. Building a network: introduction; designing the MLP; training neural networks; implementations details. Time varying systems: time varying data sets; neural networks for predicting or classifying time series; choosing the best method for the task; predicting more than one step into the future; learning separate paths through state space; recurrent networks as models of finite state automata; summary of temporal neural networks. Data collection and validation: data collection; building the training and test sets; data quality; calculating entropy values for a data set; using a forward-inverse model to serve III posed problems. Output and error analysis: introduction; what do the errors mean? error bars and confidence limits; methods for visualising errors; novelty detection; implementation details; a simple two class example; unbalanced data: a mail shot targeting example; auto-associative network novelty detection; training a network on confidence limits; an example based on credit rating. Network use and analysis: introduction; extracting reasons; traversing a network summary; calculating the derivatives; personnel selection: a worked example. Managing a neural network based project: project context; development platform; project personnel; project costs; the benefits of neural computing; the risks involved with neural computing; alternatives to a neural computing approach; project time scale; project documentation; system maintenance. Review of neural applications: introduction to part II. Neural networks and signal processing: introduction; signal processing as data preparation; pre-processing techniques for visual processing; neural filters in the Fourier and temporal domains; speech recognition; production quality control; an artistic style classifier; fingerprint analysis; summary. Financial and business modelling. (Part contents).
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About Kevin Swingler

Kevin Swingler runs a successful neural engineering consulting company called Neural Innovation, a company which won the 1994 John Logie Baird Award for Innovation. The company was also awarded a SMART award in 1995 for a neural network based software package. Dr. Swingler is also involved with research and teaching at Stirling University in Scotland.
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