Numerical Results. Artificial Neural Networks Topologies. Learning in Artificial Neural Networks. Back-Propagation Learning Rule.
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Modifications of the Classic Back-Propagation Method. Optimization Methods in Neural Networks Learning. Networks with Output Linearly Depending on Parameters. Global Optimization Methods.
Clustering Methods: Introduction. Self-Organizing Feature Map. Vector Quantization and Learning Vector Quantization. An Overview of Clustering Methods. Fuzzy Clustering Methods. A Possibilistic Approach to Clustering. Fuzzy Learning Vector Quantization. Cluster Validity. The Mamdani Fuzzy Systems. Fuzzy Systems with Parametrized Consequents. Further Investigations of Neuro-Fuzzy Systems.
Application to Chaotic Time Series Prediction. Application to the Recognition of Diabetes in Pima Indians. Application to the Iris Problem.
Application to Monk's Problems. Application to System Identification. Application to Control. Application to Channel Equalization. Show More.
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This book offers a guideline for Technology Audit exercises for the transforming innovation systems of Traditional Artificial Intelligence AI systems adopted symbolic processing as their main paradigm. Symbolic AI systems Symbolic AI systems have proved effective in handling problems characterized by exact and complete knowledge representation.
Unfortunately, these systems have very little power in dealing with imprecise, uncertain Developments in Soft Computing. Soft Computing has come of age. In particular, Artificial Neural Networks, Fuzzy Logic and Evolutionary Computing now play an important role in many domains where traditional techniques have been found wanting.
As this volume confirms, hybrid solutions that combine more Es werden die Anforderungen an die Informationstechnik aus Sicht der Neural Networks and Soft Computing: Proceedings of the. This volume presents new trends and developments in soft computing techniques. Help Centre. Track My Order. My Wishlist Sign In Join. Be the first to write a review. Add to Wishlist. Ships in 15 business days. Link Either by signing into your account or linking your membership details before your order is placed. Description Table of Contents Product Details Click on the cover image above to read some pages of this book!
Classical Sets and Fuzzy Sets. Basic Definitions and Terminology: Classical Sets. Fuzzy Sets. Operations on Fuzzy Sets. Classification of t-Norms and t-Conorms. Parameterized t-, s-Norms and Negations. Fuzzy Relations.
Fuzzy and Neuro-Fuzzy Intelligent Systems
Cylindrical Extension and Projection of Fuzzy Sets. Extension Principle. Linguistic Variable. Compositional Rule of Inference. Fuzzy Reasoning.
Canonical Fuzzy If-Then Rule. Aggregation Operation. Approximate Reasoning with Singletons. Fuzzifiers and Defuzzifiers. Numerical Results. Artificial Neural Networks Topologies. Learning in Artificial Neural Networks. Back-Propagation Learning Rule. Modifications of the Classic Back-Propagation Method. Optimization Methods in Neural Networks Learning.
Networks with Output Linearly Depending on Parameters.
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Global Optimization Methods. Clustering Methods: Introduction. Self-Organizing Feature Map. Vector Quantization and Learning Vector Quantization. An Overview of Clustering Methods.