Sinan Altug

Works (4)

Updated: July 5th, 2023 16:03

1999 journal article

A "mutual update" training algorithm for fuzzy adaptive logic control/decision network (FALCON)

IEEE TRANSACTIONS ON NEURAL NETWORKS, 10(1), 196–199.

By: S. Altug*, H. Trussell * & M. Chow*

author keywords: adaptation; artificial neural networks; fault detection and diagnosis; mutual update; training
TL;DR: In this correspondence, a training scheme is proposed for this fuzzy/neural architecture, which is based on line search methods that have long been used in iterative optimization problems, and involves synchronous update of the parameters of the architecture corresponding to input and output space partitions and rules defining the underlying mapping. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

1999 journal article

Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 46(6), 1069–1079.

By: S. Altug n, M. Chow & H. Trussell  n

author keywords: fuzzy inference systems; induction motors; knowledge extraction; motor fault diagnosis; neural/fuzzy systems
TL;DR: Two neural fuzzy (NN/FZ) inference systems, namely, fuzzy adaptive learning control/decision network (FALCON) and adaptive network based fuzzy inference system (ANFIS), with applications to induction motor fault detection/diagnosis problems are presented. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

1999 journal article

Heuristic constraints enforcement for training of and knowledge extraction from a fuzzy/neural architecture - Part I: Foundation

IEEE Transactions on Fuzzy Systems, 7(2), 143–150.

By: M. Chow*, S. Altug* & H. Trussell *

TL;DR: The paper proposes a novel method for enforcing heuristic constraints on membership functions for rule extraction from a fuzzy/neural architecture that not only ensures that the final membership functions conform to a priori heuristic knowledge, but also reduces the domain of search of the training and improves convergence speed. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

1999 journal article

Heuristic constraints enforcement for training of and rule extraction from a fuzzy/neural architecture - Part II: Implementation and application

IEEE TRANSACTIONS ON FUZZY SYSTEMS, 7(2), 151–159.

By: S. Altug n, M. Chow* & H. Trussell *

author keywords: constraint enforcement; knowledge extraction; neural-fuzzy architectures; set theory
TL;DR: The results indicate that heuristic constraint enforcement on membership functions leads to extraction of heuristically acceptable membership functions in the input and output spaces. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
16. Peace, Justice and Strong Institutions (OpenAlex)
Source: Web Of Science
Added: August 6, 2018

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