Fuzziness measure on complete hedge algebras and quantifying semantics of terms in linear hedge algebras

2007 ◽  
Vol 158 (4) ◽  
pp. 452-471 ◽  
Author(s):  
Nguyen Cat Ho ◽  
Nguyen Van Long
Keyword(s):  
Fuzziness Measure

scholarly journals Hedge Algebras: An Algebraic Approach to Domains of Linguistic Variables and Their Applicability

2017 ◽  
Vol 23 (1&2) ◽  
pp. 1
Author(s):  
Ho N.C. N.C.
Keyword(s):  
Inverted Pendulum ◽  
Algebraic Approach ◽  
Conditional Reasoning ◽  
Linguistic Variables ◽  
New Approach ◽  
Reasoning Problem ◽  
Fuzziness Measure ◽  
Linguistic Hedges

The paper is an overview on an algebraic approach to domains of linguistic variables and somefirst applications to show the applicability of this new approach. In this approach, each linguistic domain can be considered as a hedge algebra (HA for short) and based on the structure of HAs,a notion of fuzziness measure of linguistic hedges and terms can be defined. In order to apply hedge algebras to those problems, the results of which are needed, a notion of semantically quantifying mappings (SQMs) will be introduced. It shown that there is a closed connection between SQMs and fuzziness measure of hedge and primary terms (the generators of linguistic domains). To show the applicability of this approach, new met hods to solve a Fuzzy Multiple Conditional Reasoning problem, the problem of Balancing an Inverted Pendulum will be presented.

An Efficient Handwritten Character Recognition Using Quantum Multilayer Neural Network (QMLNN) Architecture

2021 ◽  
pp. 435-446
Author(s):  
Debanjan Konar ◽  
Suman Kalyan Kar
Keyword(s):  
Neural Network ◽  
Character Recognition ◽  
Input Image ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Fuzzy Input ◽  
Feed Forward Network ◽  
Network Output ◽  
Fuzziness Measure ◽  
Neighborhood Topology

This chapter proposes a quantum multi-layer neural network (QMLNN) architecture suitable for handwritten character recognition in real time, assisted by quantum backpropagation of errors calculated from the quantum-inspired fuzziness measure of network output states. It is composed of three second-order neighborhood-topology-based inter-connected layers of neurons represented by qubits known as input, hidden, and output layers. The QMLNN architecture is a feed forward network with standard quantum backpropagation algorithm for the adjustment of its weighted interconnection. QMLNN self-organizes the quantum fuzzy input image information by means of the quantum backpropagating errors at the intermediate and output layers of the architecture. The interconnection weights are described using rotation gates. After the network is stabilized, a quantum observation at the output layer destroys the superposition of quantum states in order to obtain true binary outputs.

A fuzziness measure for fuzzy numbers: Applications

1998 ◽  
Vol 94 (2) ◽  
pp. 205-216 ◽  
Author(s):  
M. Delgado ◽  
M.A. Vila ◽  
W. Voxman
Keyword(s):  
Fuzzy Numbers ◽  
Fuzziness Measure

True Color Image Segmentation by MUSIG Activation Function Using Self-Supervised QMLSONN Architecture With Context-Sensitive Thresholding

2018 ◽  
pp. 213-261
Author(s):  
Pankaj Pal ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Real Life ◽  
Activation Function ◽  
Input Image ◽  
Color Image Segmentation ◽  
Context Sensitive ◽  
True Color Image ◽  
Fuzziness Measure ◽  
True Color

In this chapter, the authors propose the true color image segmentation in real-life images as well as synthetic images by means of thresholded MUSIG function, which is learnt by quantum-formulated self-supervised neural network according to change of phase. In the initial phase, the true color image is segregated in the source module to fragment three different components—red, green, and blue colors—for three parallel layers of QMLSONN architecture. This information is fused in the sink module of QPSONN to get the preferred output. Each pixel of the input image is converted to the corresponding qubit neurons according to the phase manner. The interconnection weights between the layers are represented by qubit rotation gates. The quantum measurement at the output layer destroys the quantum states and gets the output for the processed information by means of quantum backpropagation algorithm using fuzziness measure.

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