An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions

G Leng; Anjan Ray; TM McGinnity; SA Coleman; LP Maguire; Philip Vance

An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions

G Leng, Anjan Ray, TM McGinnity, SA Coleman, LP Maguire, Philip Vance

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	International Academy, Research, and Industry Association
Pages	159-164
Number of pages	6
ISBN (Print)	978-1-61208-340-7
Publication status	Published (in print/issue) - 25 May 2014
Event	Cognitive 2014 - Venice, Italy Duration: 25 May 2014 → …

Conference

Conference	Cognitive 2014
Period	25/05/14 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions",

abstract = "In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.",

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note = "Cognitive 2014 ; Conference date: 25-05-2014",

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T1 - An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions

AU - Leng, G

AU - Ray, Anjan

AU - McGinnity, TM

AU - Coleman, SA

AU - Maguire, LP

AU - Vance, Philip

PY - 2014/5/25

Y1 - 2014/5/25

N2 - In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.

AB - In an ambient assisted living environment, raw data can often be very noisy making is difficulty to interrupt by a decision and reasoning system. To help reduce the effects of noise, we propose a decision and reasoning system which combines an interval fuzzy system and a self-organising fuzzy neural network (SOFNN) is presented in this paper. The method exploits the use of a trained standard SOFNN structure from a fuzzy neural network to initialise the proposed approach. Simulation results show that the proposed structure is more suitable for uncertain situations demonstrating a high level of robustness.

M3 - Conference contribution

SN - 978-1-61208-340-7

SP - 159

EP - 164

BT - Unknown Host Publication

PB - International Academy, Research, and Industry Association

T2 - Cognitive 2014

Y2 - 25 May 2014

ER -

An Interval Type-2 Fuzzy Neural Network for Cognitive Decisions

Abstract

Conference

UN SDGs

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