Fuzzy Relational Maps and Neutrosophic Relational Maps


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Fuzzy Relational Maps and Neutrosophic Relational Maps

For further questions please contact our TIB customer service. International conference; 8th, Fuzzy systems; ; Seoul. Table of contents conference proceedings The table of contents of the conference proceedings is generated automatically, so it can be incomplete, although all articles are available in the TIB. Computing with descriptive and veristic words: Possibility degree and sensitivity analysis in possibilistic multiobjective linear programming problems. Mutuality measures corresponding to subjective judgment of similarity and matching.

Hierarchical fuzzy control scheme using structured Takagi-Sugeno type fuzzy inference. Behavior-based fuzzy control system for a mobile robot with environment recognition by sensory-motor coordination. Fuzzy logic-based system coordination by redundant symbolization and reduction of environmental complexities. Designing breast cancer diagnostic systems via a hybrid fuzzy-genetic methodology. Medical application of fuzzy theory to the diagnostic system of tongue inspection in traditional Chinese medicine.

Fuzzy logic and knowledge based automatic control of the moving-actuator type totally-implantable artificial heart. The convexity of the set of all fuzzy implications: Fuzzy reasoning method in fuzzy rule-based systems with general and specific rules for function approximation. Nonlinear black-box modeling of electric arc furnace: A design method of stable fuzzy controller on symbolic level based on relaxed non-separate condition.

Stabilization of direct adaptive fuzzy control systems considering minimum approximation error. The virtual fuzzy automaton approach to the problem of global state evaluation in multitask control systems. A study of a radix-2 Signed-Digital fuzzy processor using the logic oriented neural networks. Implementation of MEBP learning circuitry with simple nonlinear synapse circuits.

Diagnosis of hepatobiliary disorders using rules extracted from artificial neural networks.

Foundations of Neutrosophic Logic and Set and their Applications to Information Fusion

A study on multi-layer fuzzy polynomial inference system based on an extended GMDH algorithm. A new method for avoiding abnormal conclusion for alpha -cut based rule interpolation. An approximate reasoning using probability-possibility transformation for medical expert systems.

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1. FUZZY RELATIONAL MAPS. AND NEUTROSOPHIC. RELATIONAL MAPS. W. B. Vasantha Kandasamy. Department of Mathematics. Indian Institute of. PDF | On Jan 1, , Florentin Smarandache and others published FUZZY RELATIONAL MAPS AND NEUTROSOPHIC RELATIONAL MAPS.

Upswing and stabilization control of inverted pendulum and cart system by the SIRMs dynamically connected fuzzy inference model. Microcomputer controlled buck regulator for maximum power point tracker for DC pumping system operates from photovoltaic system. New method of dealing with partially inconsistent rule bases for fuzzy logic controller.

Design of gradient descent based self-organizing fuzzy logic controller with dual outputs. Attitude control of helicopter simulator using neural network based PID controller.

Generating fuzzy control rules by a clustering algorithm based on a grey relational measure. Fuzzy identification by means of partitions of fuzzy input space and an aggregate objective function. The self-generating fuzzy algorithm with singleton output type for multi-input fuzzy variables. In Chapter One we introduce some of the basic neutrosophic algebraic structures essential for the further development of the other chapters.

Chapter Two recalls basic graph theory definitions and results which has interested us and for which we give the neutrosophic analogues.

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In this chapter we give the application of graphs in fuzzy models. An entire section is devoted for this purpose.

Fuzzy relation

Chapter Three introduces many new neutrosophic concepts in graphs and applies it to the case of neutrosophic cognitive maps and neutrosophic relational maps. The last section of this chapter clearly illustrates how the neutrosophic graphs are utilized in the neutrosophic models. The final chapter gives some problems about neutrosophic graphs which will make one understand this new subject.

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