Hybrid neural-based decision level fusion architecture: application to road traffic collision avoidance

Madani, Kurosh; Chebira, Abdennasser; Bouchefra, Kamel; Maurin, T.; Reynaud, Roger

doi:10.1117/1.602015

Cited by 6 publications

(2 citation statements)

References 7 publications

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“…For the solution of recognition problems, as well as for the formation of sign spaces and classes alphabets, while development and application of recognition algorithms by a set of signs, including parametric, nonparametric, neurocomputer and linguistic algorithms, while collection of signs statistics by means of experiment, mathematical or physical modeling that is regarded as recognition teaching, adaptation to specific conditions etc., a number of mathematics theories and methods is involved; the development of these theories and methods is connected with the advent of expert and intelligence systems: theory of fuzzy sets and theory of possibilities 1 , theory of neuro networks 2,3 , and other mathematics methods 4 . More purposefully the apparatus of algebraic and fuzzy sets of continuous logic (CL); its derivatives and generalizations, including hybrid, fuzzy predicate logic, neuro logic etc.…”

Section: Introductionmentioning

confidence: 99%

<title>Family of new operations equivalency of neuro-fuzzy logic: optoelectronic realization and applications</title>

Krasilenko¹,

Nikolsky

Yatskovsky

et al. 2002

Photonic and Quantum Technologies for Aerospace Applications IV

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The perspective of neural networks equivalental models (EM) base on vector-matrix procedure with basic operations of continuous and neuro-fuzzy logic (equivalence, absolute difference) are shown. Capacity on base EMs exceeded the amount of neurons in 2,5 times. This is larger than others neural networks paradigms. Amount neurons of this neural networks on base EMs may be 10 -20 thousands. The base operations in EMs are normalized equivalency operations. The family of new operations "equivalency" and "non-equivalency" of neuro-fuzzy logic's, which we have elaborated on the based of such generalized operations of fuzzy-logic's as fuzzy negation, t-norm and s-norm are shown. Generalized rules of construction of new functions (operations) "equivalency" which uses relations of t-norm and snorm to fuzzy negation are proposed. Among these elements the following should be underlined: 1) the element which fulfills the operation of limited difference; 2) the element which algebraic product (intensifier with controlled coefficient of transmission or multiplier of analog signals); 3) the element which fulfills a sample summarizing (uniting) of signals (including the one during normalizing). Synthesized structures which realize on the basic of these elements the whole spectrum of required operations: t-norm, s-norm and new operations -"equivalency" are shown. These realization on the basic of new multifunctional optoelectronical BISPIN-devices (MOEBD) represent the circuit with constant and pulse optical input signals. They are modeling the operation of limited difference. These circuits realize frequency-dynamic neuron models and neural networks. Experimental results of these MOEBD and "equivalency" circuits, which fulfil the limited difference operation are discussed. For effective realization of neural networks on the basic of EMs as it is shown in report, picture elements are required as main nodes to implement element operations "equivalence" ("non-equivalence") of neuro-fuzzy logic's.

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Section: Introductionmentioning

confidence: 99%

<title>Family of new operations equivalency of neuro-fuzzy logic: optoelectronic realization and applications</title>

Krasilenko¹,

Nikolsky

Yatskovsky

et al. 2002

Photonic and Quantum Technologies for Aerospace Applications IV

View full text Add to dashboard Cite

show abstract

“…For solution of recognition problems a number of mathematics theories and methods is involved; the development of these theories and methods is connected with the advent of expert and intelligence systems: theory of fuzzy sets and theory of possibilities (Zadeh, 1965), theory of neural networks (Freeman James, 1992), and other mathematics methods (Madani, 1998). More purposefully the apparatus of algebraic and fuzzy sets of continuous logic (CL); its derivatives and generalizations, including hybrid, fuzzy predicate logic, neural logic etc.…”

Section: Basic Neurological Operations Of Normalized Equivalencementioning

confidence: 99%