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Elsevier Artificial Neural Networks Type-2 Fuzzy Set - Soft Computing

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Description

This comprehensive guide explores the intersection of artificial neural networks and type-2 fuzzy set theory within soft computing frameworks. Soft computing methodologies enable intelligent systems to handle imprecision, approximate reasoning, and uncertainty whilst maintaining robustness and cost effectiveness. Neural networks excel at modelling dynamic solvers, classifying data, and predicting solutions across diverse applications. Fuzzy sets provide a natural framework for managing uncertainty in complex systems. This Morgan Kaufmann publication integrates both technologies to demonstrate practical implementations of soft computing principles in building intelligent machines, making it essential for computer scientists, engineers, and researchers working with AI and machine learning applications.

Key Features

Combines artificial neural networks with type-2 fuzzy set theory for comprehensive soft computing education.

Explains how neural networks model dynamic solvers, classify data, and predict solutions effectively.

Demonstrates fuzzy sets' natural framework for dealing with uncertainty in intelligent systems.

Covers soft computing methodologies including genetic algorithms, Bayesian networks, and rough sets.

Published by Morgan Kaufmann, a leading authority in computing and artificial intelligence literature.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
06 March 2025
Listed Since
19 July 2024

Barcode

No barcode data available

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