Artificial Intelligence may be used to solve complex technical and practical problems. Moreover, such Artificial Intelligence techniques as Fuzzy Systems, Artificial Neural Networks, Evolutionary and Genetic Algorithms have a great potential in providing optimization, prediction and management of communication system in a dynamic wireless environment. Artificial Intelligence approach provides optimized results in a challenging task of the call admission control process in cellular networks. The paper proposes a methodology for developing a genetic neuro-fuzzy controller for call admission in cellular networks. A structure of the access fuzzy controller was suggested. Linguistic variables, their terms as well as membership functions for input and output values were determined. A rule base was proposed. A Neuro-fuzzy System on the base of the fuzzy-controller was designed and simulated. A genetic algorithm was proposed for optimizing the rule base.
The application of the proposed mathematical tools, that can be used for signal processing self-generating transducers of physical quantities, are considered. Its operating mechanism is based on frequency modulation, when the frequency deviation depends on the intensity of exposure measurement parameter. It is shown that the action for leveling additive errors in the channel the signal generator is highly stable enough to use approximating second order polynomial.
In the work the non-standard system of w-parameters of the microwave quadripoles is proposed. a method of floating loads for the determination of w-parameters of microwave quadripoles is developed. the elements of the theory of the method of floating loads are proposed. new equations for determining the w-parameters of microwave quadripoles are obtained. the experimental setup for the determination of w-parameters of microwave quadripoles is developed. the results of experimental studies of w-parameters of the field transistor are obtained.
The paper proposes using a fuzzy controller in telecommunication networks for improving the scheduling process. A structure of the fuzzy controller was developed. Linguistic variables, terms and membership functions for input and output values were defined. A rules base was developed. An Adaptive Neuro-fuzzy Inference System (ANFIS) on the base of the fuzzy-controller was developed. A genetic algorithm to improve the rule base was proposed. The operation of ANFIS was simulated and trained.
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