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Supervised Learning Neural Networks

  • Patricia Melin
  • Oscar Castillo
Chapter
  • 700 Downloads
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 172)

Abstract

In this chapter, we describe the basic concepts, notation, and basic learning algorithms for supervised neural networks that will be of great use for solving pattern recognition problems in the following chapters of this book. The chapter is organized as follows: backpropagation for feedforward networks, radial basis networks, adaptive neuro-fuzzy inference systems (ANFIS) and applications. First, we give a brief review of the basic concepts of neural networks and the basic backpropagation learning algorithm. Second, we give a brief description of the momentum and adaptive momentum learning algorithms. Third, we give a brief review of the radial basis neural networks. Finally, we end the chapter with a description of the adaptive neuro-fuzzy inference system (ANFIS) methodology. We consider this material necessary to understand the new methods for pattern recognition that will be presented in the final chapters of this book.

Keywords

Membership Function Hide Layer Radial Basis Function Radial Basis Function Network Feedforward Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Patricia Melin
    • 1
  • Oscar Castillo
    • 2
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA
  2. 2.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA

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